Systems and methods to predict a future outcome at a live sport event

ABSTRACT

In an embodiment, a process to predict an outcome of a competition includes receiving time-stamped position information of participant(s), the time-stamped position information captured by a telemetry tracking system during the competition. The process includes calculating while the competition is ongoing a covariate parameter for each of one or more participants at a point in time, where each respective covariate parameter is derived from the time-stamped position information of a corresponding participant at the point in time. The process includes predicting the outcome of the competition, as of the point in time, based at least in part on (i) a difference between a calculated competitor strength of the first competitor the second competitor based on historical data associated with the competitors, and (ii) the calculated first covariate parameter(s).

CROSS REFERENCE TO OTHER APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/794,980 entitled SYSTEMS AND METHODS FOR PREDICTING A FUTUREOUTCOME AT A LIVE SPORT EVENT USING EXTENDED COVARIATES filed Jan. 21,2019, which is incorporated herein by reference for all purposes. Thisapplication claims priority to U.S. Provisional Patent Application No.62/802,182 entitled SYSTEMS AND METHODS FOR PREDICTING A FUTURE OUTCOMEAT A LIVE SPORT EVENT USING EXTENDED COVARIATES filed Feb. 6, 2019,which is incorporated herein by reference for all purposes.

BACKGROUND OF THE INVENTION

Evaluation of team strengths and players' abilities, and predictingoutcomes of sport events (e.g., football games) have an important rolein the sport industry for increasing spectators' interest andengagement. The evaluation is fundamentally based on collection andanalyses of play-by-play data. As an example, fantasy sports leagues andbetting and/or gambling games are popular forms of applications usingsuch historical play-by-play data in allowing spectators to get moreinvolved with the sporting event. Conventional approaches for evaluatingteam strengths, players, and thereby predicting outcomes of sport eventsare based on historical play-by-play data, such as data collected andpublished by the National Football League (NFL) at NFL.com. However, theplay-by-data provided by the NFL is limited to only certain players ofthe teams. Also, such data is made available only after sport events arefinished.

BRIEF SUMMARY

Techniques (including a system, a processor, and a computer programproduct) for predicting an outcome of a live sport event are disclosed.In various embodiments, a process to predict an outcome of a competitionincludes receiving time-stamped position information of participant(s),the time-stamped position information captured by a telemetry trackingsystem during the competition. The process includes calculating whilethe competition is ongoing a covariate parameter for each of one or moreparticipants at a point in time, where each respective covariateparameter is derived from the time-stamped position information of acorresponding participant at the point in time. The process includespredicting the outcome of the competition, as of the point in time,based at least in part on (i) a difference between a calculatedcompetitor strength of the first competitor the second competitor basedon historical data associated with the competitors, and (ii) thecalculated first covariate parameter(s).

Examples of an outcome of a live sport event include, withoutlimitation, an outcome of a play, an outcome of a session (quarter, halffor example) in a game, or an outcome of an entire live sport event(game), etc. Accurately and efficiently predicting the outcome while anevent is ongoing (live) is particularly challenging because conventionaltechniques are slow and typically unable to predict an outcome of a playor portion of the event prior to conclusion of the event. Otherconventional techniques output a prediction quickly, but the predictionis inaccurate sometimes because such predictions rely solely onhistorical data and do not account for the makeup (players) of a team.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

FIG. 1 is a block diagram illustrating an embodiment of a system forpredicting a future outcome at a live sport event using extendedcovariates.

FIG. 2A shows a block diagram illustrating an embodiment of a system forpredicting a future outcome at a live sport event using extendedcovariates.

FIG. 2B shows a block diagram illustrating an embodiment of a system forpredicting a future outcome at a live sport event using extendedcovariates.

FIG. 3 is a block diagram illustrating an embodiment of a trackingdevice.

FIG. 4 is a block diagram illustrating an embodiment of a trackingdevice management system.

FIG. 5 is a block diagram illustrating an embodiment of a statisticssystem.

FIG. 6 is a block diagram illustrating an embodiment of an oddsmanagement system.

FIG. 7 is a block diagram illustrating an embodiment of a user device.

FIG. 8 is a flow chart illustrating an embodiment of a process forpredicting a future outcome at a live sport event using extendedcovariates.

FIG. 9 shows an example of a 3-on-3 basketball game in which a futureoutcome is predicted using extended covariates.

FIG. 10 shows an example of a predicted outcome of a competition.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims andthe invention encompasses numerous alternatives, modifications, andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example and theinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

In various embodiments, the outcome of a live sport event is predictedusing extended covariates describing team strengths and players'abilities among other things. As further described below, the extendedcovariates are derived from telemetric data collected by a playertracking system. The examples below chiefly use the example of gridiron(American) football, but this is not intended to be limited and thetechniques may be applied to other types of competitions such as livesport events. As further described below, historical data includingtracking data may be collected during a competition and the informationapplied to a later competition to predict an outcome of the latercompetition.

FIG. 1 is a block diagram illustrating an embodiment of a system forpredicting a future outcome at a live sport event using extendedcovariates. This exemplary system 48 predicts an outcome of acompetition between a first competitor and a second competitor. Thefirst competitor includes a first set of one or more participants and asecond competitor includes a second set of one or more participants.System 48 includes communication interface 107 and processor 100.Communication interface 107 is configured to receive time-stampedposition information of one or more participants of one or both of thefirst set of participant(s) and the second set of participant(s) in thecompetition. In various embodiments, the time-stamped positioninformation is captured by a telemetry tracking system during thecompetition. In this example, the telemetry tracking system is made upof tracking device(s) 300-1 to 300-P, anchor device(s) 120-1 to 120-Q,and optionally camera(s) 140-1 to 140-S, which are managed by trackermanagement system 400 as further described below.

Processor 100 is coupled to communication interface 107 and configuredto calculate, e.g., while the present competition is ongoing, a firstcovariate parameter for each of one or more participants in one or bothof the first set of participants and the second set of participants atand/or as of a point in time. Each respective first covariate parameteris derived from the time-stamped position information of a correspondingparticipant of the first or second set of one or more participants inthe present competition at the point in time. Some examples of covariateparameters are further discussed with respect to Tables 1 and 2 below.

Processor 100 is further configured to predict the outcome of thepresent competition, as of the point in time, based at least in part on(i) a difference between a calculated competitor strength of the firstcompetitor and a calculated competitor strength of the second competitorbased at least in part on historical data associated with the first andsecond competitors, respectively, and (ii) the calculated firstcovariate parameter(s).

In various embodiments, processor 100 includes tracking managementsystem 400 for tracking a plurality of subjects and statistics system500 for managing various statistics. Tracking device management system400 facilitates managing of one or more tracking devices 300 and one ormore anchor devices 120 of the system. Statistics system 500 storesand/or generates various statistics for use in predicting an outcome ata competition such as a live sports event, providing odds for wageringon various circumstances or outcomes in the sports event, and othersimilar activities. In various embodiments, tracking management system400 and statistics system 500 comprise software engines or modulesrunning on processor 100 and/or separate or potentially separatesystems, each comprising and/or running on one or more processorscomprising processor 100.

In various embodiments, system 48 includes odds management system 600for managing odds and a plurality of user devices 700-1 to 700-R.Although odds management system 600 is shown external to processor 100,in some embodiments the odds management system is included in theprocessor. Odds management system 600 facilitates determining odds foroutcomes in a sports event and managing various models related topredicting outcomes at the live event.

In some embodiments, the system includes one or more user devices 700that facilitate end user interaction with various systems of the presentdisclosure, such as odds management system 600. Moreover, in someembodiments, system 48 includes one or more cameras 140 that capturelive images and/or video of a live event that is then utilized by thesystems of the present disclosure. In some embodiments, the cameras 140include one or more high resolution cameras. By way of non-limitingexample, the one or more high resolution cameras includes a camera witha 1080p resolution, 1440p resolution, 2K resolution, 4K resolution, or8K resolution. Utilizing a camera 140 with a high resolution allows fora video feed captured by the camera to be partitioned at a higherresolution, while also allowing for more partitions to be createdwithout a noticeable decline in image quality.

The above-identified components are interconnected, optionally through acommunications network. Elements in dashed boxes are optional combinedas a single system or device. Of course, other topologies of thecomputer system 48 are possible. For instance, in some implementations,any of the illustrated devices and systems can in fact constituteseveral computer systems that are linked together in a network, or be avirtual machine or a container in a cloud computing environment.Moreover, in some embodiments rather than relying on a physicalcommunications network 106, the illustrated devices and systemswirelessly transmit information between each other. As such, theexemplary topology shown in FIG. 1 merely serves to describe thefeatures of an embodiment of the present disclosure in a manner thatwill be readily understood to one of skill in the art.

In some implementations, the communication network 106 interconnectstracking device management system 400 that manages one or more trackingdevices 300 and one or more anchors 120, statistics system 500, oddsmanagement system 600, one or more user devices 700, and one or morecameras 140 with each other, as well as optional external systems anddevices. In some implementations, the communication network 106optionally includes the Internet, one or more local area networks(LANs), one or more wide area networks (WANs), other types of networks,or a combination of such networks.

Examples of networks 106 include the World Wide Web (WWW), an intranetand/or a wireless network, such as a cellular telephone network, awireless local area network (LAN) and/or a metropolitan area network(MAN), and other devices by wireless communication. The wirelesscommunication optionally uses any of a plurality of communicationsstandards, protocols and technologies, including Global System forMobile Communications (GSM), Enhanced Data GSM Environment (EDGE),high-speed downlink packet access (HSDPA), high-speed uplink packetaccess (HSUPA), Evolution, Data-Only (EV-DO), HSPA, HSPA+, Dual-CellHSPA (DC-HSPDA), long term evolution (LTE), near field communication(NFC), wideband code division multiple access (W-CDMA), code divisionmultiple access (CDMA), time division multiple access (TDMA), Bluetooth,Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11ac, IEEE802.11ax, IEEE 802.11b, IEEE 802.11g and/or IEEE 802.11n), voice overInternet Protocol (VoIP), Wi-MAX, a protocol for e-mail (e.g., Internetmessage access protocol (IMAP) and/or post office protocol (POP)),instant messaging (e.g., extensible messaging and presence protocol(XMPP), Session Initiation Protocol for Instant Messaging and PresenceLeveraging Extensions (SIMPLE), Instant Messaging and Presence Service(IMPS)), and/or Short Message Service (SMS), or any other suitablecommunication protocol, including communication protocols not yetdeveloped as of the filing date of this document.

In various embodiments, processor 100 includes a machine learning engine210 (not shown in FIG. 1) that facilitates the prediction of the outcomeof a competitions. The next figure describes an example of processor 100that includes a machine learning engine in greater detail.

FIGS. 2A and 2B show a block diagram illustrating an embodiment of asystem for predicting a future outcome at a live sport event usingextended covariates. As depicted in FIG. 2A, an array of anchor devices120 receives telemetry data 230 from one or more tracking devices 300.In order to minimize error in receiving the telemetry from the one ormore tracking devices 300, the array of anchor devices 120 preferablyincludes at least three anchor devices. Inclusion of at least threeanchor devices 120 within the array of anchor devices allow for eachping (e.g., telemetry data 230) received from a respective trackingdevice 300 to be triangulated using the combined data from the at leastthree anchor that receive the respective ping. Additional details andinformation regarding systems and methods for receiving pings fromtracking devices and the optimization thereof will be described in moredetail infra, with particular reference to at least FIGS. 3 and 4.

In the example shown, the telemetry data 230 that is received by thearray of anchors 120 from the one or more tracking devices 300 includespositional telemetry data 232. The positional telemetry data 232provides location data for a respective tracking device 300, whichdescribes a location of the tracking device within a spatial region. Insome embodiments, this positional telemetry data 232 is provided as oneor more Cartesian coordinates (e.g., an X coordinate, a Y coordinate,and/or Z a coordinate) that describe the position of each respectivetracking device 300, although any coordinate system (e.g., polarcoordinates, etc.) that describes the position of each respectivetracking device 300 is used in alternative embodiments.

The telemetry data 230 that is received by the array of anchors 120 fromthe one or more tracking devices 300 includes kinetic telemetry data234. The kinetic telemetry data 234 provides data related to variouskinematics of the respective tracking device. In some embodiments, thiskinetic telemetry data 234 is provided as a velocity of the respectivetracking device 300, an acceleration of the respective tracking device,and/or a jerk of the respective tracking device. Further, in someembodiments one or more of the above values is determined from anaccelerometer (e.g., accelerometer 317 of FIG. 3) of the respectivetracking device 300 and/or derived from the positional telemetry data232 of the respective tracking device. Further, in some embodiments thetelemetry data 230 that is received by the array of anchors 120 from theone or more tracking devices 300 includes biometric telemetry data 236.The biometric telemetry data 236 provides biometric information relatedto each subject associated with the respective tracking device 300. Insome embodiments, this biometric information includes a heart rate ofthe subject, temperature (e.g., a skin temperature, a temporaltemperature, etc.), and the like.

In some embodiments, the array of anchors 120 communicates the abovedescribed telemetry data (e.g., positional telemetry 232, kinetictelemetry 234, biometric telemetry 236) to a telemetry parsing system240. Accordingly, in some embodiments the telemetry parsing system 240communicates the telemetry data (e.g., stream of data 244) to a machinelearning engine 210 and/or a real time data packager 246 for furtherprocessing and analysis.

In some embodiments, the real time data packager 246 synchronizes one ormore data sources (e.g., streaming data 244 from telemetry parsingsystem 240, game statistics input system 250, machine learning engine210, etc.) by using one or more timestamps associated with therespective data. For instance, in some embodiments the data sourcesprovide data that is associated with a real world clock timestamp (e.g.,an event occurred at and is associated with a real world time of 1:17P.M.). In some embodiments, the data sources provide data that isassociated with a game clock timestamp related to a live sports event(e.g., an event occurred with 2 minutes and 15 seconds remaining in thesecond quarter). Moreover, in some embodiments the data sources providedata that is associated with both the real world clock timestamp and thegame clock timestamp. Synchronization of the data sources via timestampsallows for a designer of the present disclosure to provide services withan additional layer of accuracy, particularly with betting and wageringon outcomes at a live event. For instance, in some embodiments dataprovided to a user device 700 (e.g., streaming data 280 and/or directdata 282 of FIG. 2B) describes the wagering (e.g., odds) on a next playin a football game. In order to determine if an end user of the userdevice 700 places a wager within a predetermined window of time (e.g.,before the snap of the ball of the next play), the game clock and realworld time data received from the user device and/or communicated to theuser device are analyzed and the wager is either validated, rejected, orheld for further consideration.

In some embodiments, machine learning engine 210 receives data fromvarious sources of the present disclosure in order to predict a futureoutcome at a live sporting event and generate statistics for analysisand use. For instance, in some embodiments the data sources of themachine learning engine 210 includes a positional data formationclassifier 212, hereinafter “neural net,” that provides informationrelated to various configurations and formations of players at any givenpoint of time in game. For instance, in some embodiments the formationclassifier 212 parses the telemetry data 230 to analyze pre-snapformations of players. The analyses of the pre-snap telemetry data 230allows for the formation classifier 212 to determine various states andconditions of the game, such as a down of a game, a positional ruleviolation within a game (e.g., off-sides, illegal motion, etc.), and thelike. Moreover, in some embodiments the formation classifier 212analyzes telemetry data 230 that is received subsequent the start of theplay in order to further generate data and information related to howeach formation evolves (e.g., an expected running route versus an actualrunning route, an expected blocking assignment versus an action blockingassignment, a speed of a player throughout a play, a distance betweentwo players throughout a play, etc.).

In some embodiments, machine learning engine 210 includes a historicaltraining data store 214. Historical data store 214 provides historicaldata and information related to each particular sport (e.g., sportshistorical data 508 of FIG. 5), each particular team associated with theparticular sport (e.g., team historical data 510 of FIG. 5), and/or eachparticular player associated with the particular sport and/or team(e.g., player historical data 514 of FIG. 5). In some embodiments, thisdata is initially used as a training data set for the machine learningengine 210. However, the present disclosure is not limited thereto asthis data may also be used to further augment the features and servicesprovided by the machine learning engine 210 and other systems of thepresent disclosure.

Further, in some embodiments the machine learning engine 210 includes avariety of models 220 that are utilized to predict a future outcome of asporting event and provide analysis of the sporting event. In someembodiments, the models 220 of the machine learning engine 210 includean expected points model 222. The expected points model 222 provides alikelihood of receiving points for a particular play at the event via anumerical value. In some embodiments, the models 220 of the machinelearning engine 210 include a win probability model 224 that provideseither a likelihood of each participating team of the event to win or alikelihood of any given point spread between the winning and losingteams at the event. Furthermore, in some embodiments the models 220 ofthe machine learning engine 210 include a player based wins abovereplacement (WAR) model 226. The WAR model 226 provides a contributionvalue a respective player adds to their corresponding team (e.g., player1 provides a value of 1 to a respective team and player two provides avalue of 2 to the respective team, therefore player two is worth more tothe respective team).

In some embodiments, machine learning engine 210 include a situationstore 228. The situation store 228 is a cache of various situationaldetails and/or statistics that is accessed rapidly during a real gamescenario. Rapid access to the situation store 228 prevents lag thatwould otherwise be induced from querying different databases and systems(e.g., positional data formation classifier 212, historical trainingdata 214, etc.) in order to obtain the same information. Additionaldetails and information regarding the machine learning engine and thecomponents therein, including the various above described data storesand models, will be described in more detail infra, with particularreference to at least FIGS. 5 and 6.

Machine learning engine 210 communicates various odds and outputs of thevarious databases and models therein to an odds management system 600.In communicating with the machine learning engine 210, the oddsmanagement system 600 provides various wagers and predictive odds forfuture events at a sporting event to the user devices 700, while alsoupdating these odds in real time to reflect current situations andstatistics of a game.

As depicted in FIG. 2B, in some embodiments system 48 includes a gamestatistics input system 250. The game statistics input system 250 isconfigured for providing at least in play data 254, which, in examplecase of football, describes a state of the game during a given play(e.g., a weak side receiver ran a post route), as well as end of playdata 256, which describes a state of the game after a given play (e.g.,a play resulted in a first down at the opponents 42-yard line). In someembodiments, the data of the statistics input system 250 is associatedwith the world and game clock 242, and accordingly is communicated tothe telemetry parsing system 240 and/or the machine learning engine 210.In some embodiments the game statistics input system 250 is subsumed bythe formation classifier 212.

In some embodiments, various data is communicated to an applicationprogramming interface (API) server 260. This data may include streamingdata 244, end of play data 256, data from the odds management system600, or a combination thereof. Accordingly, the API server 260facilitates communication between various components of the system 48,one or more user devices 700, and a master statistics database 270 inorder to provide various features and services of the present disclosure(e.g., a stream of the game, a request for statistics, placing a wageron a play, etc.). Communication between the API server 260 and the oneor more user devices 700 includes providing streaming data 280 and/ordirect data 282 to each respective user device 700 through thecommunications network 106, as well as receiving various requests 284from each respective user device. By way of non-limiting example,streaming data 280 includes tracking “telemetry” data including xyzcoordinates of players or accelerometer data of players, direct data 282includes clock, score, or remaining timeouts.

In some embodiments, the master statistics database 270 includes some orall of the statistics known to the machine learning engine 210 that areobtainable to a user. The master statistics database is updatedregularly such as at the end of every play or every few plays. Forinstance, in some embodiments only a portion of the statistics known tothe machine learning engine 210 is desired to be obtainable by a user,and thus is stored in the master statistics database 270. However, thepresent disclosure is not limited thereto. For instance, in someembodiments the master statistics database 270 is subsumed by themachine learning engine 270. Elements in dashed boxes are optionalcombined as a single system or device.

Now that an infrastructure of the system 48 has been generallydescribed, an exemplary tracking device 300 will be described withreference to FIG. 3.

FIG. 3 is a block diagram illustrating an embodiment of a trackingdevice. In various implementations, the tracking device, hereinafteralso a “tracker,” includes one or more processing units (CPUs) 374, amemory 302 (e.g., a random access memory), one or more magnetic diskstorage and/or persistent device 390 optionally accessed by one or morecontrollers 388, a network or other communications interface (which mayinclude RF circuitry) 384, an accelerometer 317, one or more optionalintensity sensors 364, an optional input/output (I/O) subsystem 366, oneor more communication busses 313 for interconnecting the aforementionedcomponents, and a power supply 376 for powering the aforementionedcomponents. In some implementations, data in memory 302 is seamlesslyshared with non-volatile memory 390 using known computing techniquessuch as caching. In some implementations, memory 302 and/or memory 390may in fact be hosted on computers that are external to the trackingdevice 300 but that can be electronically accessed by the trackingdevice 300 over an Internet, intranet, or other form of network orelectronic cable (illustrated as element 106 in FIG. 1) using networkinterface 384.

In various embodiments, the tracking device 300 illustrated in FIG. 3includes, in addition to accelerometer(s) 317, a magnetometer and/or aGPS (or GLONASS or other global navigation system) receiver forobtaining information concerning a location and/or an orientation (e.g.,portrait or landscape) of the tracking device 300.

It should be appreciated that the tracking device 300 illustrated inFIG. 3 is only one example of a device that may be used for obtainingtelemetry data (e.g., positional telemetry 232, kinetic telemetry 234,and biometric telemetry 236) of a corresponding subject, and that thetracking device 300 optionally has more or fewer components than shown,optionally combines two or more components, or optionally has adifferent configuration or arrangement of the components. The variouscomponents shown in FIG. 3 are implemented in hardware, software,firmware, or a combination thereof, including one or more signalprocessing and/or application specific integrated circuits.

Memory 302 of the tracking device 300 illustrated in FIG. 3 optionallyincludes high-speed random access memory and optionally also includesnon-volatile memory, such as one or more magnetic disk storage devices,flash memory devices, or other non-volatile solid-state memory devices.Access to memory 302 by other components of the tracking device 300,such as CPU(s) 374 is, optionally, controlled by the memory controller388.

In some embodiments, the CPU(s) 374 and memory controller 388 are,optionally, implemented on a single chip. In some other embodiments, theCPU(s) 374 and memory controller 388 are implemented on separate chips.

Radio frequency (RF) circuitry of network interface 384 receives andsends RF signals, also called electromagnetic signals. In someembodiments, the RF circuitry 384 converts electrical signals to fromelectromagnetic signals and communicates with communication networks andother communications devices, such as the one or more anchor devices 120and/or the tracking device management system 400, via theelectromagnetic signals. The RF circuitry 384 optionally includeswell-known circuitry for performing these functions, including but notlimited to an antenna system, a RF transceiver, one or more amplifiers,a tuner, one or more oscillators, a digital signal processor, a CODECchipset, a subscriber identity module (SIM) card, memory, and so forth.On some embodiments, the RF circuitry 384 optionally communicates withthe communication network 106.

In some embodiments, the network interface (including RF circuitry) 384operates via ultra-wide band (UWB) technology, which allows for thetracking device 300 to communicate with an array of anchor devices 120in a crowded spatial region, such as a live sporting event. In someembodiments, the tracking device 300 transmits a low power (e.g.,approximately 1 milliwatt (mW)) signal at a predetermined centerfrequency (e.g., 6.55 GHz 200 mHz, yielding a total frequency range oftransmission of approximately about 6.35 GHz to about 6.75 GHz). As usedherein, these communications and transmissions are hereinafter referredto as a “ping.” For a discussion of UWB, see Jiang et al, 2000,“Ultra-Wide Band technology applications in construction: a review,”Organization, Technology and Management in Construction 2(2), 207-213.

In some embodiments, the power supply 358 optionally includes a powermanagement system, one or more power sources (e.g., a battery, arecharging system, a power failure detection circuit, a power converteror inverter, a power status indicator (e.g., a light-emitting diode(LED)) and any other components associated with the generation,management and distribution of power in such tracking devices 300. Insome embodiments, the telemetry data 230 includes information related tothe power supply 358 of the respective tracking device 300, such as abattery consumption or an expected period of time until the trackingdevice requires more power.

In some implementations, the memory 302 of the tracking device 300 fortracking a respective subject stores:

-   -   an operating system 304 (e.g., ANDROID, iOS, DARWIN, RTXC,        LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such        as VxWorks) includes various software components and or drivers        for controlling and managing general system tasks (e.g., memory        management, storage device control, power management, etc.) and        facilitates communication between various hardware and software        components;    -   a tracking device identifier module 305 that stores data used to        identify the respective tracking device 300 including a tracking        device identifier 306 and an optional tracking device group        identifier 307; and    -   a tracking device ping module 308 that stores data and        information related to a ping rate of the respective tracking        device, the tracking device ping module 308 including:        -   an instantaneous ping rate 310 that describes a current ping            rate a respective tracking device 300 is currently operating            at,        -   a minimum ping rate 312 that describes a minimum ping rate a            respective tracking device 300 may operate at,        -   a maximum ping rate 314 that describes a maximum ping rate a            respective tracking device 300 may operate at,        -   a threshold ping rate 316 that describes a minimum ping rate            a respective tracking device 300 may operate at, and        -   a variable ping rate flag 318.

The tracking device identifier module 305 stores information thatrelates to identifying the respective tracking device 300 from aplurality of tracking devices (e.g., tracking device 1 300-1, trackingdevice 2 300-3, . . . , tracking device P 300-P). In some embodiments,the information stored by the tracking device identifier module 305includes a tracking device identifier (ID) 306 that includes a unique ID(e.g., a serial number or a code) representing the respective trackingdevice 300. In some embodiments, the tracking device ID module 305includes a tracking device group ID 307 that designates the respectivetracking device 300 to one or more groups of tracking devices (e.g.,tracking device group 418-2 of FIG. 4). Further, in some embodimentspings communicated by the respective tracking device 300 includes dataof the tracking device ID module 305, allowing for an array of anchordevices 120 to identify pings received from more than one trackingdevice. Additional details and information regarding the grouping of atracking device 300 will be describe in more detail infra, withparticular reference to at least FIG. 4.

The tracking device ping module 308 stores data and information relatedto various ping parameters and conditions of respective tracking device300, as well as facilitating management of the ping. For instance, insome embodiments the tracking device ping module 308 manages aninstantaneous ping rate 310 of the respective tracking device 300 (e.g.,managing an instantaneous ping rate 310 to be 10 Hertz (HZ)). In someembodiments, the tracking device 300 is configured with one or more pingrate limits, including one or more both of a minimum ping rate 312 and amaximum ping rate 314, that define a maximum and a minimum ping ratethat the tracking device 300 may transmit pings. For instance, in someembodiments the minimum ping rate 312 and/or the maximum ping rate 314may be set by the tracking device management system 400 based upon oneor more of bandwidth limitations, a number of active tracking devices300, and a type of expected activity (e.g., a sport and/or event types,an expected subject activity, etc.). When configured with one or bothping rate limits, the tracking device ping module 308 operates to adjustthe instantaneous ping rate 310 between the minimum ping rate 312 andthe maximum ping rate 314. Thus, automatic optimization of trackingmanagement system 400 may be used in combination with automatic pingrate adjustment of tracking device 300. In some embodiments, trackingdevice ping module 308 is configured to compare detected motion fromaccelerometer 317 to a predefined threshold 316. Accordingly, the pingmodule 308 increases the instantaneous ping rate 310 in accordance witha determination that the detected motion is greater than predefinedthreshold 316 (e.g., until the instantaneous ping rate 310 reaches themaximum ping rate 314). Likewise, the ping module 308 decreases theinstantaneous ping rate 310 (e.g., until the instantaneous ping rate 310reaches the minimum ping rate 312) in accordance with a determinationthat the detected motion is less than the threshold ping rate 316.

In some embodiments, the ping module 310 includes a variable ping rateflag 318, which is configured (e.g., set wirelessly) by the trackingdevice management system 400, that determines whether ping module 308automatically, or not, changes the instantons ping rate 310 based upondetermined activity. For example, the tracking device management system400 may set variable ping rate flag 318 to “false” for one or moretracking devices 300 that is associated with a player not currentlyparticipating on the field of play, wherein instantaneous ping rate 310remains at a low rate even if the player is actively warming up forexample. Tracking device management system 400 sets variable ping rateflag 318 to “true” for one or more players that is activelyparticipating on the field of play. Furthermore, in some embodimentseach tracking device 300 is dynamically configured based upon a locationof the respective tracking device. For instance, in accordance with adetermination that a tracking device 300 is within a field of play(e.g., if a player is actively participating in a game) as opposed to adetermination that the tracking device is off the field of play (e.g.,if a player is not actively participating in a game).

Utilizing the tracking device ping model 308 and/or the sensor (e.g.,accelerometer 317 and/or optional sensors 364) within tracking device300 increases reliability of the system 48 (e.g., the array of anchors120, the telemetry parsing system 240, the tracking device managementsystem 400, etc.) to track subjects disposed with the tracking device.

As previously described, in some embodiments each tracking device 300provides telemetry data 230 that is received and communicated by variousanchors 120 that are proximate to the respective tracking device 300.This telemetry data includes positional telemetry data 232 (e.g., X, Y,and/or Z coordinates), kinetic telemetry data 234 (e.g., velocity,acceleration, and/or jerk), and/or biometric telemetry data 236 (e.g.,heart rate, physical attributes of a player such as shoulder width,etc.).

In some embodiments, each subject in the game is equipped with more thanone tracking device 300 in order to increase the accuracy of the datareceived from the tracking devices about the subject. For instance, insome embodiments the left shoulder and the right shoulder of arespective subject are both equipped with a tracking device 300, eachsuch tracking device functioning normally and having line of site to atleast a subset of the anchors 120. Accordingly, in some embodiments thedata from the left and right tracking devices 300 have their telemetrydata 230 combined to form a single time-stamped object. This singleobject combines positional data from both tracking devices 300 to createa center line representation of a position of the respective player.Moreover, this center line calculated position provides a more accuraterepresentation of the center of a player's position on the playingfield. Further, using the relative positional data from two trackingdevices 300 positioned on the left and right shoulders of a player,prior to creating the single player object as described above, allowsthe system 48 to determine a direction (e.g., a rotation) that theplayer is facing. In various embodiments, including rotational datagreatly eases the task of creating avatars from data created byrecording telemetry data 230 during a game and/or establishingsophisticated covariates that can be used to better predict futureevents in the game or the final outcome of the game itself.

In some embodiments, the tracking device 300 has any or all of thecircuitry, hardware components, and software components found in thedevice depicted in FIG. 3. In the interest of brevity and clarity, onlya few of the possible components of the tracking device 300 are shown tobetter emphasize the additional software modules that are installed onthe tracking device 300.

FIG. 4 is a block diagram illustrating an embodiment of a trackingdevice management system. Tracking device management system 400 isassociated with one or more tracking devices 300 and anchors 120. Thetracking device management system 400 includes one or more processingunits (CPUs) 474, a peripherals interface 470, a memory controller 488,a network or other communications interface 484, a memory 402 (e.g.,random access memory), a user interface 478, the user interface 478including a display 482 and an input 480 (e.g., a keyboard, a keypad, atouch screen, etc.), an input/output (I/O) subsystem 466, one or morecommunication busses 413 for interconnecting the aforementionedcomponents, and a power supply system 476 for powering theaforementioned components.

In some embodiments, the input 480 is a touch-sensitive display, such asa touch-sensitive surface. In some embodiments, the user interface 478includes one or more soft keyboard embodiments. The soft keyboardembodiments may include standard (QWERTY) and/or non-standardconfigurations of symbols on the displayed icons.

It should be appreciated that tracking device management system 400 isonly one example of a system that may be used in engaging with varioustracking devices 300, and that tracking device management system 400optionally has more or fewer components than shown, optionally combinestwo or more components, or optionally has a different configuration orarrangement of the components. The various components shown in FIG. 4are implemented in hardware, software, firmware, or a combinationthereof, including one or more signal processing and/or applicationspecific integrated circuits.

Memory 402 optionally includes high-speed random access memory andoptionally also includes non-volatile memory, such as one or moremagnetic disk storage devices, flash memory devices, or othernon-volatile solid-state memory devices. Access to memory 402 by othercomponents of the management system 400, such as CPU(s) 474 is,optionally, controlled by memory controller 488.

Peripherals interface 470 can be used to couple input and outputperipherals of the management system to CPU(s) 474 and memory 402. Theone or more processors 474 run or execute various software programsand/or sets of instructions stored in memory 402 to perform variousfunctions for the management system 400 and to process data.

In some embodiments, peripherals interface 470, CPU(s) 474, and memorycontroller 488 are, optionally, implemented on a single chip. In someother embodiments, they are, optionally, implemented on separate chips.

In some embodiments, power system 476 optionally includes a powermanagement system, one or more power sources (e.g., battery, alternatingcurrent (AC)), a recharging system, a power failure detection circuit, apower converter or inverter, a power status indicator (e.g., alight-emitting diode (LED), etc.) and any other components associatedwith the generation, management and distribution of power in portabledevices.

As illustrated in FIG. 4, memory 402 of the tracking device managementsystem preferably stores:

-   -   an operating system 404 (e.g., ANDROID, iOS, DARWIN, RTXC,        LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such        as VxWorks) includes various software components and or drivers        for controlling and managing general system tasks (e.g., memory        management, storage device control, power management, etc.) and        facilitates communication between various hardware and software        components; and    -   a tracking device manager module 406 for facilitating management        of one or more tracking devices 300, the tracking device manager        module including:        -   a tracking device identifier store 408 for storing pertinent            information related to each respective tracking device 410-1            including a tracking device identifier 306 and a tracking            device ping rate 414, and        -   a tracking device grouping store 416 for facilitating            management of or more tracking device groups 307.

The tracking device identifier store 408 includes information related toeach respective tracking device 410-1, including the tracking deviceidentifier (ID) 306 for each respective tracking device 300 as well as atracking device group 307 to which the respective tracking device isassociated. For instance, in some embodiments a first tracking devicegroup 307-1 is associated with the left shoulder of each respectivesubject and a second tracking device group 307-2 is associated with aright shoulder of each respective subject. Moreover, in some embodimentsa third tracking device group 307-3 is associated with a first position(e.g., receiver, defensive end, safety, etc.) of each respective subjectand a fourth tracking device group 307-4 is associated with a secondposition. Grouping 307 of the tracking devices 300 allows for aparticular group to be designated with a particular ping rate (e.g., afaster ping rate for running backs). Grouping 307 of the trackingdevices 300 also allows for a particular group to be isolated from othertracking devices that are not associated with the respective group,which is useful in viewing representations of the telemetry data 230provided by the tracking devices of the group. Additional informationrelated to tracking devices and tracking device management systems isfound in U.S. Pat. No. 9,950,238, entitled “Object Tracking SystemOptimization and Tools.”

FIG. 5 is a block diagram illustrating an embodiment of a statisticssystem. Statistics system 500 stores and determines various statisticsin accordance with the present disclosure. The statistics system 500includes one or more processing units (CPUs) 574, peripherals interface570, memory controller 588, a network or other communications interface584, a memory 502 (e.g., random access memory), a user interface 578,the user interface 578 including a display 582 and an input 580 (e.g., akeyboard, a keypad, a touch screen, etc.), input/output (I/O) subsystem566, one or more communication busses 513 for interconnecting theaforementioned components, and a power supply system 576 for poweringthe aforementioned components.

In some embodiments, the input 580 is a touch-sensitive display, such asa touch-sensitive surface. In some embodiments, the user interface 578includes one or more soft keyboard embodiments. The soft keyboardembodiments may include standard (e.g., QWERTY) and/or non-standardconfigurations of symbols on the displayed icons.

It should be appreciated that statistics system 500 is only one exampleof a system that may be used in staring and determining variousstatistics, and that statistics system 500 optionally has more or fewercomponents than shown, optionally combines two or more components, oroptionally has a different configuration or arrangement of thecomponents. The various components shown in FIG. 5 are implemented inhardware, software, firmware, or a combination thereof, including one ormore signal processing and/or application specific integrated circuits.

Memory 502 optionally includes high-speed random access memory andoptionally also includes non-volatile memory, such as one or moremagnetic disk storage devices, flash memory devices, or othernon-volatile solid-state memory devices. Access to memory 502 by othercomponents of the statistics system 500, such as CPU(s) 574 is,optionally, controlled by memory controller 588.

Peripherals interface 570 can be used to couple input and outputperipherals of the management system to CPU(s) 574 and memory 502. Theone or more processors 574 run or execute various software programsand/or sets of instructions stored in memory 502 to perform variousfunctions for the statistics system 500 and to process data.

In some embodiments, peripherals interface 570, CPU(s) 574, and memorycontroller 588 are, optionally, implemented on a single chip. In someother embodiments, they are, optionally, implemented on separate chips.

In some embodiments, power system 576 optionally includes a powermanagement system, one or more power sources (e.g., battery, alternatingcurrent (AC)), a recharging system, a power failure detection circuit, apower converter or inverter, a power status indicator (e.g., alight-emitting diode (LED), etc.) and any other components associatedwith the generation, management and distribution of power in portabledevices.

As illustrated in FIG. 5, memory 502 of the remote user devicepreferably stores:

-   -   an operating system 504 (e.g., ANDROID, iOS, DARWIN, RTXC,        LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such        as VxWorks) includes various software components and or drivers        for controlling and managing general system tasks (e.g., memory        management, storage device control, power management, etc.) and        facilitates communication between various hardware and software        components;    -   a positional formation classifier 212 for determining and        analyzing formations of players;    -   a historical training data store 214 for storing various        statistics related to each sport 508, wherein each sport 508        including various team historical data 510 for one or more teams        512, as well as various player statistics 514 for one or more        players 516; and    -   a situational store 228 for storing data related to formations        of players and game situations.

The positional formation classifier 212 (sometimes simply called aformation classifier) provides information related to various states andformations of players at any given point of time in game. For instance,in some embodiments the formation classifier 212 parses telemetry data230 in order to determine pre-snap formations. Accordingly, once aformation is determined and telemetry data 230 is parsed, sub-categoriesof the formation may be determined (e.g., an I-formation with differentsub-categories defining different running backs). Moreover, in someembodiments the formation classifier 212 acts as a virtual referee anddetermines if infractions have occurred within a game or play, such as aplayer being off-sides, a neutral zone infraction, an illegal motion, anillegal formation, and the like. In some embodiments, the formationclassifier 212 includes one or more tables of various formations in afootball game, such as a first table of offensive formations, a secondtable of defensive formations, and a third table of special teamsformations. In some embodiments, the above table of formations providessome or all of the formations described by Table 1, Table 2, and Table3.

TABLE 1 Exemplary Offensive Football Formations Exemplary FormationDouble wing formation Empty backfield formation Goal line formation Iformation Pistol formation Pro set formation Short punt formationShotgun formation Single set back formation Single wing formation Tformation Tackle spread formation V formation Victory formation Wing Tformation Wishbone formation

TABLE 2 Exemplary Defensive Football Formations Exemplary Formation 38formation 46 formation 2-5 formation 3-4 formation 4-3 formation 4-4formation 5-2 formation 5-3 formation 6-1 formation 6-2 formationSeven-man line formation Nickle formation Dime formation Quarterformation Half dollar formation

TABLE 3 Exemplary Special Teams Football Formations Exemplary FormationField goal formation Kick return formation Kickoff formation Puntformation

Additionally, in some embodiments the formation classifier 212determines a ball carrier by comparing telemetry data 230 provided bythe ball and telemetry data of a player that is closest to the ball.Likewise, in some embodiments determining which team has possession ofthe ball is conducted in a similar manner. Furthermore, in someembodiments the formation classifier 212 determines if a player iswithin a boundary of a game by analyses the telemetry data 230 extractedfrom the player and comparing this with the known boundaries of thefield of play. In this way, the formation classifier 212 parsestelemetry data 230 to provide a box score and/or automatic colorcommentary of a game.

While the formation classifier 212 is labeled a “neural net” it will beappreciated that the formation classifier 212 module does not have toperform classification of team formation using a neural networkclassifier. In some embodiments the formation classifier 212 module doesin fact make use of any classification scheme that can discern a teamformation from telemetry data. For instance, in some embodimentsformation classifier 212 makes use of a nearest neighbor algorithm toperform the classification of team formation. In other embodimentsformation classifier 212 makes use of clustering to perform theclassification of team formation. In some embodiments the elucidation ofthe formation class by formation classifier 212 is used as a covariatein statistical models that predict the outcome of a current live game(e.g., win/loss, point spread, etc.) as disclosed with respect tomethods and features described with respect to FIG. 8.

In more detail, in some embodiments, the formation classifier 212 isbased on a logistic regression algorithm, a neural network algorithm, asupport vector machine (SVM) algorithm, a Naive Bayes algorithm, anearest-neighbor algorithm, a boosted trees algorithm, a random forestalgorithm, or a decision tree algorithm.

By way of non-limiting example the formation classifier 212 is based ona logistic regression algorithm, a neural network algorithm, a supportvector machine (SVM) algorithm, a Naive Bayes algorithm, anearest-neighbor algorithm, a boosted trees algorithm, a random forestalgorithm, or a decision tree algorithm. When used for classification,SVMs separate a given set of binary labeled data training set with ahyper-plane that is maximally distant from the labeled data. For casesin which no linear separation is possible, SVMs can work in combinationwith the technique of ‘kernels’, which automatically realizes anon-linear mapping to a feature space. The hyper-plane found by the SVMin feature space corresponds to a non-linear decision boundary in theinput space. Tree-based methods partition the feature space into a setof rectangles, and then fit a model (like a constant) in each one. Insome embodiments, the decision tree is random forest regression. Onespecific algorithm that can serve as the formation classifier 212 forthe instant methods is a classification and regression tree (CART).Other specific decision tree algorithms that can serve as the formationclassifier 212 for the instant methods include, but are not limited to,ID3, C4.5, MART, and Random Forests.

In some embodiments, the historical data store 214 stores statisticsrelated to each sport 508, each team 510 within the sport league, aswell as the respective players 512. As previously described, in someembodiments the data stored in the historical data store 214 is utilizedas a training set of data for machine learning engine 210 and/orformation classifier 212. For instance, in some embodiments the datastored in the historical data store 214 is utilized as an initial dataset at a start of a league, as in inferred from other data sets ofsimilar league (e.g., using college football stats if a player is aprofessional rookie), or utilized to create data points if a newstatistic is being generated (e.g., a previously unknown statisticbecomes relevant). Furthermore, in some embodiments data from apreviously played game is stored within the historical data store 214.

In some embodiments, the situation store 228 includes data stored in oneor more databases of the machine learning engine 210 as a cache ofinformation. This cache of the situation store 228 allows for data to bequeried for and utilized rapidly, rather than having to query eachrespective database. In some embodiments, the situation store 288creates a new cache of data for each respective game. However, thepresent disclosure is not limited thereto.

FIG. 6 is a block diagram illustrating an embodiment of an oddsmanagement system. Odds management system 600 stores and determinesvarious odds in accordance with the present disclosure. The oddsmanagement system 600 includes one or more processing units (CPUs) 674,peripherals interface 670, memory controller 688, a network or othercommunications interface 684, a memory 602 (e.g., random access memory),a user interface 678, the user interface 678 including a display 682 andan input 680 (e.g., a keyboard, a keypad, a touch screen, etc.),input/output (I/O) subsystem 666, one or more communication busses 613for interconnecting the aforementioned components, and a power supplysystem 676 for powering the aforementioned components.

In some embodiments, the input 680 is a touch-sensitive display, such asa touch-sensitive surface. In some embodiments, the user interface 778includes one or more soft keyboard embodiments. The soft keyboardembodiments may include standard (QWERTY) and/or non-standardconfigurations of symbols on the displayed icons.

It should be appreciated that odds management system 600 is only oneexample of a system that may be used in staring and determining variousstatistics, and that the odds management system 600 optionally has moreor fewer components than shown, optionally combines two or morecomponents, or optionally has a different configuration or arrangementof the components. The various components shown in FIG. 6 areimplemented in hardware, software, firmware, or a combination thereof,including one or more signal processing and/or application specificintegrated circuits.

Memory 602 optionally includes high-speed random access memory andoptionally also includes non-volatile memory, such as one or moremagnetic disk storage devices, flash memory devices, or othernon-volatile solid-state memory devices. Access to memory 602 by othercomponents of the odds management system 600, such as CPU(s) 674 is,optionally, controlled by memory controller 688.

Peripherals interface 670 can be used to couple input and outputperipherals of the management system to CPU(s) 674 and memory 602. Theone or more processors 674 run or execute various software programsand/or sets of instructions stored in memory 602 to perform variousfunctions for the odds management system 600 and to process data.

In some embodiments, peripherals interface 670, CPU(s) 674, and memorycontroller 688 are, optionally, implemented on a single chip. In someother embodiments, they are, optionally, implemented on separate chips.

In some embodiments, power system 676 optionally includes a powermanagement system, one or more power sources (e.g., battery, alternatingcurrent (AC)), a recharging system, a power failure detection circuit, apower converter or inverter, a power status indicator (e.g., alight-emitting diode (LED), etc.) and any other components associatedwith the generation, management and distribution of power in portabledevices.

As illustrated in FIG. 6, memory 602 of the remote user devicepreferably stores:

-   -   an operating system 604 (e.g., ANDROID, iOS, DARWIN, RTXC,        LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such        as VxWorks) includes various software components and or drivers        for controlling and managing general system tasks (e.g., memory        management, storage device control, power management, etc.) and        facilitates communication between various hardware and software        components;    -   a modelling engine 200 for storing one or more prediction or        outcome models, the modelling engine including:        -   an expected points model module 222 for determining an            expected points value of a scenario in a game,        -   a win probability model 224 for determining a probably of            winning a game, and        -   a player based wins above replacement model module 226 for            determining;        -   a real time game situation module 614 for receiving and            communicating information related to a game currently being            conducted; and        -   an odds management module 616 for facilitation management of            various odds and betting systems.

As previously described, the modelling engine 200 includes variousalgorithms and models utilized for generating statistics and predictingoutcomes at a sports event. In some embodiments, these models includethe expected points model 222 that provides a numerical value for eachplay of a game. For instance, if a drive in a game that results in atouchdown has plays that include a 5-yard rush, a 94-yard pass, and a1-yard rush, even though the 1-yard rush resulted in the touchdown the94-yard pass has a much more significant role in the drive. Thus, insome embodiments the 5-yard rush is allocated an expected points valueof 0.5, the 94-yard pass is allocated an expected points value of 5.5,and the 1-yard rush is allocated an expected points value of 1, withhigh values indicating more important or game defining plays. In someembodiments modelling engine 200 uses the telemetry data collected inaccordance with the present disclosure to predict the outcome of a game(e.g., win/loss, point spread, etc.) as disclosed with respect tomethods and features described with respect to FIG. 8.

In some embodiments, the real time game situation module 614 receivesinformation related to situations occurring in a game. This informationis then utilized in adjusting various weights and values in the abovedescribed models. For instance, if a quarterback rolls his ankle and hasto take every play from a shotgun position, this immobility of thequarterback will be reflected in the game models 220 through the realtime game situation module 614.

FIG. 7 is a block diagram illustrating an embodiment of a user device.User device is a remote user device 700 associated with an end user inaccordance with the present disclosure. The user device 700 includes oneor more processing units (CPUs) 774, peripherals interface 770, memorycontroller 788, a network or other communications interface 784, amemory 702 (e.g., random access memory), a user interface 778, the userinterface 778 including a display 782 and an input 780 (e.g., akeyboard, a keypad, a touch screen, etc.), input/output (I/O) subsystem766, an optional accelerometer 717, an optional GPS 719, optional audiocircuitry 772, an optional speaker 760, an optional microphone 762, oneor more optional sensors 764 such as for detecting intensity of contactson the user device 700 (e.g., a touch-sensitive surface such as atouch-sensitive display system of the device 700) and/or an opticalsensor, one or more communication busses 713 for interconnecting theaforementioned components, and a power supply system 776 for poweringthe aforementioned components.

In some embodiments, the input 780 is a touch-sensitive display, such asa touch-sensitive surface. In some embodiments, the user interface 778includes one or more soft keyboard embodiments. The soft keyboardembodiments may include standard (QWERTY) and/or non-standardconfigurations of symbols on the displayed icons.

It should be appreciated that the user device 700 is only one example ofa device of a multifunction device that may be used by end users, andthat the user device 700 optionally has more or fewer components thanshown, optionally combines two or more components, or optionally has adifferent configuration or arrangement of the components. The variouscomponents shown in FIG. 7 are implemented in hardware, software,firmware, or a combination thereof, including one or more signalprocessing and/or application specific integrated circuits.

Memory 702 optionally includes high-speed random access memory andoptionally also includes non-volatile memory, such as one or moremagnetic disk storage devices, flash memory devices, or othernon-volatile solid-state memory devices. Access to memory 702 by othercomponents of the user device 700, such as CPU(s) 774 is, optionally,controlled by memory controller 788.

Peripherals interface 770 can be used to couple input and outputperipherals of the management system to CPU(s) 774 and memory 702. Theone or more processors 774 run or execute various software programsand/or sets of instructions stored in memory 702 to perform variousfunctions for the user device 700 and to process data.

In some embodiments, peripherals interface 770, CPU(s) 774, and memorycontroller 788 are, optionally, implemented on a single chip. In someother embodiments, they are, optionally, implemented on separate chips.

In some embodiments, audio circuitry 772, speaker 760, and microphone762 provide an audio interface between a user and the device 700. Theaudio circuitry 772 receives audio data from peripherals interface 770,converts the audio data to an electrical signal, and transmits theelectrical signal to speaker 760. Speaker 760 converts the electricalsignal to human-audible sound waves. Audio circuitry 772 also receiveselectrical signals converted by microphone 762 from sound waves. Audiocircuitry 772 converts the electrical signal to audio data and transmitsthe audio data to peripherals interface 770 for processing. Audio datais, optionally, retrieved from and/or transmitted to memory 702 and/orRF circuitry 784 by peripherals interface 770.

In some embodiments, power system 776 optionally includes a powermanagement system, one or more power sources (e.g., battery, alternatingcurrent (AC)), a recharging system, a power failure detection circuit, apower converter or inverter, a power status indicator (e.g., alight-emitting diode (LED), etc.) and any other components associatedwith the generation, management and distribution of power in portabledevices.

As illustrated in FIG. 7, memory 702 of the remote user devicepreferably stores:

-   -   an operating system 704 (e.g., ANDROID, iOS, DARWIN, RTXC,        LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such        as VxWorks) includes various software components and or drivers        for controlling and managing general system tasks (e.g., memory        management, storage device control, power management, etc.) and        facilitates communication between various hardware and software        components;    -   an electronic address 706 that is used to identify a particular        user device during communications with various systems and        devices of the present disclosure;    -   a user information store 708 that stores pertaining information        related to the respective user associated with the corresponding        user device 700, such as user access information including        usernames, user passwords, access tokens, etc.;    -   a game feed module 710 for viewing various representations of a        game including a whiteboard feed module 712, an avatar feed        module 714, and a video feed module 716 as well as viewing        various statistics related to the game; and    -   a wager module 718 that facilitates placing wagers on game        scenarios.

In some embodiments wager module 718 uses the telemetry data collectedin accordance with the present disclosure to predict the outcome of acurrent game using extended covariants (e.g., win/loss, point spread,etc.), as disclosed with respect to methods and features described withrespect to FIG. 8. In some embodiments, wager module 718 uses thetelemetry data collected in accordance with the present disclosure toprovide odds for future game events in a current live game.

Now that a general topology of the system 48 has been described, methodsfor predicting an outcome of a present game using extended covariateswill be described with reference to, at least, FIGS. 1 through 7 andFIG. 9. FIG. 9 shows an example of a 3-on-3 basketball game in which afuture outcome is predicted using extended covariates. In this example,the first competitor is a first team, the second competitor is a secondteam, the first set of participant(s) includes a plurality ofparticipants, the second set of participant(s) includes a plurality ofparticipants, and the present competition is a game. More specifically,participants 1-3 belong to the first competitor and participants 4-6belong to the second competitor.

FIG. 8 is a flow chart illustrating an embodiment of a process forpredicting a future outcome at a live sport event using extendedcovariates. This process may be implemented by processor 100 incooperation with the other devices described above. The process can beperformed to predict an outcome of a competition between a firstcompetitor that includes a first set of one or more participants and asecond competitor that includes a second set of one or moreparticipants.

The process begins at 802 by receiving time-stamped position informationof one or more participants of one or both of the first set ofparticipant(s) and the second set of participant(s) in the competition,the time-stamped position information captured by a telemetry trackingsystem during the competition. Referring to FIG. 9, the telemetry datafor each participant is his x,y coordinate position.

An example of a telemetry tracking system is the tracking devicemanagement system 400 facilitating managing of one or more trackingdevices 300 and one or more anchor devices 120 of the system, describedabove with respect to FIG. 1. The telemetry tracking system includes aplurality of tracking devices configured to provide a plurality ofsignals. Each respective participant in the first and second pluralityof participants is uniquely and independently associated with at least apair of tracking devices in the plurality of tracking devices. Thetelemetry tracking system also includes a set of three or more receivers(anchor devices) configured to receive the plurality of signals.

The telemetry tracking system also includes a computer system (e.g., thetracking device management system 400 in FIG. 4) configured todetermine, on a recurring basis using the received plurality of signals,a time-stamped position for each player of the and second plurality ofplayers, thereby obtaining time-stamped position information in the formof an independent plurality of time-stamped positions for eachrespective player in the first and second plurality of players. In someembodiments, a tracking device is attached to player's gear, e.g., ashoulder pad, or a helmet. In some embodiments, a tracking device isattached to each of the shoulder pads. Such configuration of the pair ofthe tracking devices is configured to provide telemetry data includingdirection that the respective player is facing, as well as telemetrydata capturing rotational movement and/or directional movement. Forexample, the telemetric tracking system captures not only a runningdirection but also if the player is running facing forward, sideways orbackwards. The telemetric tracking system also captures turns,rotations, falls, etc. of a player. The tracking device includes a powersource (e.g., a battery), a transmitter for transmitting wirelesssignals and means for attaching the tracking device to player's gear(e.g., a strap). The set of three or more receivers are located to havereception across the predefined area, e.g., a football field. In someembodiments, a tracking device is also attached to the ball, e.g.,providing information about the speed of the ball, or a distancetravelled by the ball during a throw or a kick. In various embodiments,the time-stamped position information captured by a telemetry trackingsystem includes position data of a ball or other equipment other than aplayer.

In some embodiments, the plurality of signals conforms to anUltra-wideband (UWB) standard. Each signal in the plurality of signalshas a bandwidth of greater than 500 MHz or a fractional bandwidth equalto or greater than 0.20. Each signal in the plurality of signals iswithin 3.4 GHz to 10.6 GHz. Each tracking device in the plurality oftracking devices has a signal refresh rate of between 1 Hz and 60 Hz(e.g., each tracking device emits 1, 5, 10, 15, 20 signals per second).Each tracking device of the plurality of tracking devices sends a uniquesignal identifying a respective tracking device, or optionally,biometric data specific to a respective player associated with therespective tracking device. In some embodiments, a tracking device isconfigured to communicate with a biometric monitoring device (e.g., aheart rate monitoring device attached to a respective player) and thebiometric data includes biometric data captured by the biometricmonitoring device. In some embodiments, the unique signal includesinformation about the respective tracking device, such as battery level.

Each time-stamped position in the independent plurality of time-stampedpositions for a respective player of the first or second plurality ofplayers includes a xyz-coordinate of the respective player with respectto a predefined space. In some embodiments, the xyz-coordinate has anaccuracy ranging from ±5 centimeters to ±30 centimeters. In someembodiments, the xyz-coordinate has an accuracy of ±15 centimeters. Insome embodiments, the league is a football league, the present game is afootball game, and the predefined space is a football field at which thepresent game is being played.

Returning to FIG. 8, at 804, the process calculates a first covariateparameter (e.g., covariates listed in Table 4 or Table 5) for each ofthe participant(s) at a point in time, where each respective firstcovariate parameter is derived from the time-stamped positioninformation of a corresponding participant of the first or second set ofone or more participants in the present competition at the point intime. The covariate parameter is calculated while the competition isongoing in some embodiments.

In various embodiments, the first covariate parameter corresponding to afirst participant included in the first set of participant(s) isdetermined based at least in part on position information of the firstparticipant relative to one or more participants included in the secondset of participant(s). The first covariate parameter is calculated atleast in part by using position information to calculate a derivativewith respect to time including at least one of velocity, acceleration,and jerk.

At 806, the process predicts the outcome of the competition, as of thepoint in time, based at least in part on (i) a difference between acalculated competitor strength of the first competitor and a calculatedcompetitor strength of the second competitor and (ii) the calculatedfirst covariate parameter(s). In various embodiments, the differencebetween a calculated competitor strength of the first competitor and acalculated competitor strength of the second competitor is calculatedbased at least in part on historical data associated with the first andsecond competitors, respectively.

The outcome of the present game can be expressed in a variety of ways.In various embodiments, the outcome of the present game is a predictedwinner. In such instances, the outcome is provided as values for each ofthe teams. For example, the value for team j as a value “1” if team jwins the present game, “0.5” if the present game ends in a tie, and a“−1” if team j loses the present game and the value for team k as avalue “1” if team k wins the present game, “0.5” if the present gameends in a tie, and a “−1” if team k loses the present game.

In various embodiments, the outcome is a predicted final scoredifference at the end of the present game from the perspective of eitherof the competing teams. In various embodiments, the outcome is apredicted final score difference at the end of the present game from theperspective of either of the competing teams.

FIG. 10 shows an example of a predicted outcome of a competition. FIG.10 corresponds to the FIG. 9, which is a 3-on-3 basketball game, andshows the probability of the game resulting in various pointdifferentials labeled on the x-axis. In this scenario, at 3:12 into thegame, the collected telemetry data indicates that the first competitoris stronger than the second competitor so the normal distribution iscentered around +5 points meaning that the first competitor is likely tomore likely than not to either tie or win by up to 10 points. Thiscontinuous distribution can be updated throughout the game and naturallymay change as player strengths and team strengths change over the courseof the game. As further described herein, the factors affecting playerand team strengths are modeled by extended covariates.

The strength of a team and differences between teams can be evaluated invariety of ways. One way to evaluate team strength θ=(θ₁, θ₂, . . . ,θ_(J)) (J=total number of teams, e.g., 32 for the NFL) uses historicalwin/loss data as discussed in “Handbook of Statistical Methods andAnalyses in Sports,” Chapter 5 titled “Estimating Team Strength in theNFL,” by Glickman and Stern, Taylor & Francis, Dec. 20, 2016.

The difference in team strengths can be evaluated while consideringextended covariates. For example, a covariate parameter is derived fromtime-stamped position information of a corresponding player in thepresent game. Using the example of football, the players include aquarterback (QB), a running back (RB), a wide receiver (WR), a tight end(TE), a center (C), an offensive guard (OG), an offensive tackle (OT), amiddle linebacker (MLB), an outside linebacker (OLD), a defensive end(DE), a defensive tackle (DT), a cornerback (CB), a safety (S), a kicker(K), a holder (H), a long snapper (LS), a punter (P) or kick/puntreturner (KR/PR).

In various embodiments, the process predicts the outcome including bycomparing a first value for the first covariate parameter calculated forthe first competitor with a second value for the first covariateparameter calculated for the second competitor.

Comparing the first value for the first covariate parameter with thesecond value for the first covariate parameter includes calculating adifference between the first value and the second value.

For example, the process calculates the difference between the firstcompetitor strength and the second competitor strength based at least inpart on the historical data, wherein the historical data includeswin/loss data among a plurality of competitors comprising a set ofcompetitors. In various embodiments, the process retrieves, from acomputer system (e.g., distributed computing system 48 described abovewith respect to FIG. 1A), win/loss data for a plurality of historicalgames (e.g., the historical training data 214), for each respective pairof participating teams in the plurality of teams.

In various embodiments, the prediction of the outcome of the presentgame, as of a time point, is performed by evaluating a continuousdistribution centered at a point that is determined by the differencebetween team strengths (e.g., based upon the win-loss data), and eachcovariate parameter. Examples of a continuous distribution include anormal distribution, a t-distribution, a unimodal continuousdistribution, and a real-valued distribution, among others.

A team strength θ_(j) of an arbitrary team j is considered to be ascalar parameter that is evaluated based on historical game results, anda prediction for a game outcome between teams j and k depends onθ_(j)-θ_(k). In other words, The difference between the calculation ofthe team strength of the first team (e.g., team j) for the present gameand the calculation of the team strength of the second team (e.g., teamk) for the present game is computed as θ_(j)-θ_(k). In a set of n games,y_(i) is an outcome of game i played between teams j_(i) and k_(i), y isa vector of all historical game outcomes (y=y₁, y₂, . . . , y_(n)),where the outcome)), is defined as a final score difference in the gamefrom the perspective of team j_(i).

Team strength θ_(j) is estimated by minimizing the sum of squareddifferences as shown in Equation 1:

SS((θ|y)=Σ_(i=1) ^(n)(y _(i)−(θ_(j) _(i) −θ_(k) _(i) ))²  (1)

Team strength θ_(j) may be interpreted as team j's mean margin ofvictory plus the mean strength of the opponents. Larger θ_(j)corresponds to a relatively better team than smaller θ_(j). Equation 1may be represented in matrix form by defining matrix X as an n×J matrixencoding the identities of teams competing across the n games such thatthe i^(th) tow of X encodes information about game i. The competingteams are indicated as elements set to 1 and −1, where remainingelements are set to 0. An example of matrix X is defined as:

$X = \begin{pmatrix}1 & 0 & {- 1} & 0 & 0 & \ldots & 0 \\0 & 0 & 1 & 0 & {- 1} & \ldots & 0 \\{- 1} & 0 & 0 & 0 & 1 & \ldots & 0 \\\vdots & \vdots & \vdots & \vdots & \vdots & \ddots & \vdots \\0 & {- 1} & 0 & 0 & 0 & 0 & 1\end{pmatrix}$

where the first game includes teams 1 and 3, the second game includesteams 3 and 5, etc. A vector describing the strength differences foreach game is formed from Xθ

${\begin{pmatrix}1 & 0 & {- 1} & 0 & 0 & \ldots & 0 \\0 & 0 & 1 & 0 & {- 1} & \ldots & 0 \\{- 1} & 0 & 0 & 0 & 1 & \ldots & 0 \\\vdots & \vdots & \vdots & \vdots & \vdots & \ddots & \vdots \\0 & {- 1} & 0 & 0 & 0 & 0 & 1\end{pmatrix}\begin{pmatrix}\theta_{1} \\\theta_{2} \\\vdots \\\theta_{j}\end{pmatrix}} = \begin{pmatrix}{\theta_{1} - \theta_{3}} \\{\theta_{3} - \theta_{5}} \\{\theta_{5} - \theta_{1}} \\\vdots \\{\theta_{j} - \theta_{2}}\end{pmatrix}$

which can be re-expressed in matrix notation as Equation 2:

SS((θ|y)=(y−Xθ)′(y−Xθ).  (2)

Equation 2 is the sum of squared residuals for linear regression withmatrix X and the unique least-squares estimate {circumflex over (θ)} ofθ is given as {circumflex over (θ)}=(X′X)⁻¹X′y when X′X is invertible.However, X′X is not invertible because the columns of X are linearlydependent as the sum of elements across every row is 0. The lineardependence of X can be addressed by imposing linear constraint on θ. Theconstraint does not change the essential solution to the optimization ofθ, but rather forces the selection of one solution among the infinitenumber in the unconstrained version. The constraint can be implementedby re-expressing Xθ as X*θ_(−J), where θ_(−J) is the vector of lengthJ−1 of team strengths with θ_(j) removed and X* is the n×(J−1) designmatrix that accounts for the linear constraint implemented on θ. Thisrequires construction of a J×(J−1) contrast matrix W satisfying X*=XW.The relation implies that Equation 3 is satisfied when Wθ_(−J)=θ:

X*θ _(−j) =XWθ _(−j) =Xθ,  (3)

The least-squares estimate {circumflex over (θ)}_(−J) is given byequation 4:

=(X*′ ^(X*))⁻¹ X*′y  (4)

Any least-squares estimate of θ satisfies X′Xθ-X′y, where X′y is thevector of J elements in which the jth element is the sum of the scoredifferences for the games team j won les the sum of the scoredifferences of the games team j lost. For θj, Equation 4 can be solvedas Equation 5a for j, and similarly as Equation 5b for k:

$\begin{matrix}{{\theta_{j} = {\frac{\underset{q \neq j}{\Sigma}n_{jq}\theta_{q}}{\underset{q \neq j}{\Sigma}n_{jq}} + \frac{{\underset{i\text{:}j\mspace{14mu} {wins}}{\Sigma}{y_{i}}} - {\underset{i\text{:}j\mspace{14mu} {losses}}{\Sigma}{y_{i}}}}{\underset{q \neq j}{\Sigma}n_{jq}}}},} & \left( {5\; a} \right) \\{{\theta_{k} = {\frac{\underset{q \neq k}{\Sigma}n_{kq}\theta_{q}}{\underset{q \neq k}{\Sigma}n_{kq}} + \frac{{\underset{i\text{:}k\mspace{14mu} {wins}}{\Sigma}{y_{i}}} - {\underset{i\text{:}k\mspace{14mu} {losses}}{\Sigma}{y_{i}}}}{\underset{q \neq k}{\Sigma}n_{kq}}}},} & \left( {5\; b} \right)\end{matrix}$

n_(jq) is the number of times the first team j and a team q in theleague have competed in the plurality of historical games, and

n_(kq) is the number of times the second team k and a team q in theleague have competed in the plurality of historical games.

The model described herein uses an approach to evaluate scoredifferences of historical games by normal distributions centered at thedifference in team strength parameters, (e. g., θ_(j)-θ_(k)). The normaldistribution provides a continuous approximation to the probabilitydistribution of score differences that is convenient to model. Thedistribution model fits historical win/loss data between teams j and kto fit a model expressed by Equation 6:

y _(i) ˜N(θ_(j) _(i) −θ_(k) _(i) ,σ²),  (6)

where y_(i) is the score difference for game I with teams j_(i) andk_(i) competing, θ_(j) is the calculation of the team strength of team jupon the win/loss data of historical games, θ_(k) is the calculation ofthe team strength of team k based upon the win/loss data of historicalgames (see, Equations 5a and 5b above) and σ² is the residual variance.Equation 6 has a mean defined by the win/loss data of historical games.The normal distribution described by Equation 6 is centered at a scoredifference depending on the historical win/loss results. For example,Equation 6 is centered at 0 in a situation where teams j and k haveequal number of wins in historical games where the teams have competedagainst each other (e.g., the teams have equal evaluated team strengthsbased on the historical win/loss data). In such cases the predictedprobability for team j to win a present game is 50%. As another example,Equation 6 is centered away from the center when either of the teamshave more wins that the other team (e.g., one team has a higher teamstrength than the other team based on the historical win/loss data).Equation 6 may be expressed in a vector form by Equation 7:

y˜N(Xθ,θ ² I),  (7)

where X is defined in Equation 2 and I is the identity matrix ofdimension n. An unbiased estimate of the residual variance σ² isprovided by Equation 8, including {circumflex over (θ)} with thelinearly constrained estimate provided in Equation 5:

$\begin{matrix}{{\overset{\hat{}}{\sigma}}^{2} = {\frac{1}{n - \left( {J - 1} \right)}\left( {y - {X\mspace{14mu} \overset{\hat{}}{\Theta}}} \right)^{\prime}{\left( {y - {X\mspace{14mu} \overset{\hat{}}{\Theta}}} \right).}}} & (8)\end{matrix}$

The estimated covariance matrix for J-dimensional 0 is computed as shownin Equation 9:

({circumflex over (θ)})=

(W{circumflex over (θ)} _(−j))=W

(W{circumflex over (θ)} _(−j))W′=

W(X*′X*)⁻¹ W.  (9)

The model described herein is extended in various embodiments toincorporate game-specific or team-specific covariate informationincluding internal and external covariates (e.g., extended covariates).As used herein, external covariates include parameters that mayinfluence the end results of a game, but are external to team'sstrength. The external covariates include, for example, weather orschedule related parameters, or location of the game (home vs. away). Asused herein, internal covariates include parameters describing a team'sstrength, such as parameters related to team's performance.

In various embodiments, extending the above described model withcovariates includes determining average values of specific variables forthe competing teams, and considering the difference between the averageswhen evaluating team strengths. For example, the model includesdetermining average rushing yards for teams j and k, and considering thedifference between the average rushing yards. Equation 6 can be modifiedto include covariates h, described with vector h_(i), as shown inEquation 10:

N(θ_(j)−θ_(k) +h _(i),σ²),  (10)

where σ² is the variance of θ_(j)−θ_(k)+h_(i) in the normal, θ₁ is thecalculation of the team strength of team j upon the win/loss data ofhistorical games, θ_(k) is the calculation of the team strength of teamk based upon the win/loss data of historical games, and h_(i) is aquantification of the covariate parameter. In various embodiments, thequantification of the covariate parameter as in Equation 10 shifts thecenter of the normal distribution described by Equation 6. For example,if covariate h_(i) corresponds to a value that is more favorable to teamj, covariate h_(i) shifts the center of the normal distribution towardthe probability that team j will win and if covariate corresponds to avalue that is more favorable to team k, covariate h_(i) shifts thecenter of the normal distribution toward the probability that team kwill win.

In order to evaluate a win probability from the normal distribution ofEquation 10 including the covariate, in various embodiments Equation 10is reduced to a model of standard normal distribution with Z-formula:

$\begin{matrix}{{Z = \frac{\left( {\theta_{j} - \theta_{k} + h_{i}} \right) - \mu}{\sigma}},} & (11)\end{matrix}$

where μ is the mean of θ_(j)−θ_(k)+h_(i) and μ is the standard deviation(i.e., a square root of variance σ² Z-values of a standard normaldistribution are associated with specific cumulative probabilities,e.g., using standard Z-score tables (e.g., see, “Statistical Reasoning,”Chapter 7.1 “The Normal Probability Table,” by G. Smit, Alyn and Bacon,Needham Mass., 1991). Equation 10 may be expressed in matrix form as:

${\left. y \right.\sim{N\left( {{\left( X \middle| H \right)\ \begin{pmatrix}\theta \\\beta_{h}\end{pmatrix}},\delta^{2}} \right)}},$

where H is the matrix with rows h_(i) and β_(h) is a vectorcorresponding to h_(i). Equation 10 is a normal linear model, andtherefore inference for the parameters may be obtained throughleast-squares regression, taking into account the collinearity with X*described above. Similarly to Equation 9 above, the covariate matrix ofestimates for {circumflex over (θ)} and {circumflex over (β)} can beobtained by defining

$\overset{\hat{}}{\eta} = {{{()}\mspace{14mu} {and}\mspace{14mu} } = {{()}\mspace{14mu} {and}}}$${W = \begin{pmatrix}W & 0 \\0 & I_{h}\end{pmatrix}},$

where I_(h) is an identity matrix having dimensions equal to the numberof variables in h. Then the covariance matrix of estimates becomesEquation (12):

({circumflex over (η)})=

({circumflex over (η)}_(−j))={tilde over (W)}

({circumflex over (η)}_(−j)){tilde over (W)}′.  (12)

In some embodiments the continuous distribution is modeled as a normaldistribution. The distribution is centered on the pointθ_(j)+θ_(k)+h_(i), and the distribution is evaluated asN(θ_(j)+θ_(k)+h_(i), σ²) (see, Equation 10), where θ_(j) is thecalculation of the team strength of the first team based upon thewin/loss data for the plurality of historical games, θ_(k) is thecalculation of the team strength of the second team based upon thewin/loss data for the plurality of historical games, h_(i) is aquantification of the first covariate parameter, and σ² is the varianceof −/+ in the normal distribution. For example, the win/loss data forthe plurality of historical games includes sport historical data 510described above with respect to FIG. 5.

In some embodiments,

$\begin{matrix}{{\theta_{j} = {\frac{\underset{q \neq j}{\Sigma}n_{jq}\theta_{q}}{\underset{q \neq j}{\Sigma}n_{jq}} + \frac{{\underset{i\text{:}j\mspace{14mu} {wins}}{\Sigma}{y_{i}}} - {\underset{i\text{:}j\mspace{14mu} {losses}}{\Sigma}{y_{i}}}}{\underset{q \neq j}{\Sigma}n_{jq}}}},} & \left( {5\; a} \right) \\{{\theta_{k} = {\frac{\underset{q \neq k}{\Sigma}n_{kq}\theta_{q}}{\underset{q \neq k}{\Sigma}n_{kq}} + \frac{{\underset{i\text{:}k\mspace{14mu} {wins}}{\Sigma}{y_{i}}} - {\underset{i\text{:}k\mspace{14mu} {losses}}{\Sigma}{y_{i}}}}{\underset{q \neq k}{\Sigma}n_{kq}}}},} & \left( {5\; b} \right)\end{matrix}$

where n_(jq) is the number of times the first team j and a team q in theleague have competed in the plurality of historical games, and n_(kq) isthe number of times the second team k and a team q in the league havecompeted in the plurality of historical games. In some embodiments thefirst covariate parameter is selected from Table 4 above for a footballgame. In some embodiments more than one covariate parameter is used. Insome such embodiments, some of the covariate parameters do not requirethe telemetry data. An example of a covariate that does not require thetelemetry data is an age or draft pick number of a player.

For example, player historical data 514 described above with respect toFIG. 5 includes a plurality of covariate parameters for players 1 to Wof each team. Each respective covariate parameter in the plurality ofcovariate parameters is derived from time-stamped position informationof the corresponding player in the present game through the first timepoint.

In some embodiments, the continuous distribution is modeled as a normaldistribution. The difference between the calculation of the teamstrength of the first team for the present game and the calculation ofthe team strength of the second team for the present game is computed asθ_(j)−θ_(k), the distribution is centered on the pointθ_(j)−θ_(k)+h_(i), and the distribution is evaluated asN(θ_(j)−θ_(k)+h_(i), σ²) (see, Equation 10 above), where θ_(j) is thecalculation of the team strength of the first team based upon thewin/loss data for the plurality of historical games, θ_(k) is thecalculation of the team strength of the second team based upon thewin/loss data for the plurality of historical games, h_(i) is acollective quantification of each covariate parameter in the firstplurality of covariate parameters, and σ² is the variance ofθ_(j)−θ_(k)+h_(i) in the normal distribution.

In some embodiments,

$\begin{matrix}{{\theta_{j} = {\frac{\underset{q \neq j}{\Sigma}n_{jq}\theta_{q}}{\underset{q \neq j}{\Sigma}n_{jq}} + \frac{{\underset{i\text{:}j\mspace{14mu} {wins}}{\Sigma}{y_{i}}} - {\underset{i\text{:}j\mspace{14mu} {losses}}{\Sigma}{y_{i}}}}{\underset{q \neq j}{\Sigma}n_{jq}}}},} & \left( {{see},{{Equation}\mspace{14mu} 5\; a}} \right) \\{{\theta_{k} = {\frac{\underset{q \neq k}{\Sigma}n_{kq}\theta_{q}}{\underset{q \neq k}{\Sigma}n_{kq}} + \frac{{\underset{i\text{:}k\mspace{14mu} {wins}}{\Sigma}{y_{i}}} - {\underset{i\text{:}k\mspace{14mu} {losses}}{\Sigma}{y_{i}}}}{\underset{q \neq k}{\Sigma}n_{kq}}}},} & \left( {{see},{{Equation}\mspace{14mu} 5\; b}} \right)\end{matrix}$

where n_(jq) is the number of times the first team j and a team q in theleague have competed in the plurality of historical games, and n_(kq) isthe number of times the second team k and a team q in the league havecompeted in the plurality of historical games. In some embodiments, thepresent game is a football game and each covariate parameter in thefirst plurality of covariate parameters is selected from Table 4 orTable 5.

Extending a prediction model to include covariates derived fromparticipant-specific information, such as positional data generated by atelemetry tracking system as described herein and/or historicalstatistics for specific participants, is disclosed.

Table 4 includes examples of internal covariates derived from aplay-by-play data derived from a telemetry tracking system in someembodiments, such as a telemetry tracking system described with respectto FIGS. 1-7 for a football game. For example, a quarterback of a teamis evaluated by covariates including aggressiveness and/or distancebetween a player and the quarterback per pass attempt. A wide receiverof a team is evaluated by covariates including cushion between thereceiver and defensive backs, a distance travelled, a percentageoutbreaking route, and yardage per route. A tight end of a team isevaluated by covariates including pulls, a percentage outbreaking route,missed blocks, and a distance travelled. In some embodiments, theinternal covariates include variables derived from historicalplay-by-play data, such as that published by the NFL.

In various embodiments, the covariate parameters can be based on one ormore of telemetry of a present competition, historical telemetry,telemetry associated with one or more competitors, and/or externalfactors (external to one or more competitors) such as weather, amongother things.

Table 5 summarizes variables published by the NFL used for evaluatingplayers and/or team strength. In some embodiments, the internalcovariates for evaluating the team strengths include (i) aseason-to-date summary of the passing yards, (ii) a season-to-datesummary of the rushing yards, (iii) a season-to-date fumble rate, and(iv) a season-to-date interception rate.

TABLE 4 Covariates derived from play-by-play telemetric data. Statistic(Covariate) Derivation Analytical Use Aggressiveness Determined, atleast, by Determines probability of of player comparing completion rateof quarterback attempting a pass QB with respect to a distance in a highrisk situation etc. between a receiver and a defensive player. AverageDetermined, at least, by Determines player fatigue velocity, derivativesof positional throughout a game, determines acceleration, telemetry dataof each optimal offensive and defensive jerk respective player or playermatchups, agility of a accelerometer. player, etc. Ball distanceDetermined, at least, by Determines lateral movement in positionaltelemetry data. comparison to actual yardage (e.g.., a lateral vs aforward pass), etc. Ball speed Determined, at least, by Determinesquarterback derivative of positional throwing speed, etc. telemetry dataof the ball. Blocking Determined, at least, by Determines expectedblocking assignment comparing formation of players assignments topredict a screen, expectations before each play, throughout a rushinggap, etc. each player, and/or after each play to determine each expectedblocking assignment for a respective formation. Burst speed Determined,at least, by Determines how quickly a from line of telemetry datareceived from rusher can break through the scrimmage each player aftercrossing the line of scrimmage (e.g., a lower line of scrimmage. burstyields a lower probability of sack), etc. Coverage type Determined, atleast, by Determines probability of a comparing telemetry data for zonecoverage or man coverage each respective offensive and/ by offensiveformation and/or or defensive formation. defensive formation, etc.Cushion between Determined, at least, by Determines probability and/orplayers comparing telemetry data from size of a distance between two anoffensive player and a or more respective players in a defensive playerfor a play (e.g., probability a respective play. receiver creates a gapbetween a defensive player), etc. Defenders in Determined, at least, byDetermines probability of Box telemetry data from one or formation,coverage type, and more defensive players at a expected formationprogression start of a play. during a play, etc. Distance betweenDetermined, at least, by Determines pressure on player and comparingtelemetry data from quarterback, effectiveness of quarterback per one ormore players and the offensive line, etc. pass attempt quarterback.Distance between Determined, at least, by Determines best matchupsrelative players comparing telemetry data from between players, pressureon two or more players. quarterback, etc. Distance Determined, at least,by Determines lateral movement travelled telemetry data from a (e.g.., anorth to south running respective player. back versus a speed back),total distance covered by a player per play, per game, etc. Double-teamDetermined, at least, by Determines double team percentage telemetrydata from two or efficiency per player and/or per more respectiveplayers. opponent, probability of a double team for each formation, etc.Positional Determined, at least, by Determines normalized heat mapsmapping telemetry data over progression of each player's a period oftime position on the field for each respective play and/or game.Formation success Determined, at least, by Determines success rate of inhuddle and/or comparing telemetry data of using a huddle and/or hurry upin hurry-up one or more players at a offensive for each player, eachsituation start of a play with a formation, etc. result of the play.Broken tackles Determined, at least, by Determines probability a playercomparing telemetry data of will break a tackle, determines two or moreplayers during a optimal tackling position (e.g., respective play. ahigh tackle, a low tackle, etc.), etc. Hurdles Determined, at least, byDetermines probability of a Expectancy comparing telemetry data ofplayer attempting a hurdle (e.g., two or more players during a a hurdlein an open field, a respective play (e.g., two quarterback leap),success rate opposing players having same of hurdling, etc. X, Ytelemetry data but different Z telemetry data at a particular point intime. Defender Determined, at least, by Determines probability acoverage type mapping defensive telemetry defender using a press dataover a period of time coverage, a deep coverage, for each respectiveplay. for each formation. Max speed Determined, at least, by Determinesoptimal matchups detecting a highest velocity against respective playersof a respective player. and/or respective routes, player fatigue, etc.Missed blocks Determined, at least, by Determines a missed blockcomparing telemetry data of occurrences. two opposing players over aperiod of time during a play. Probability of Determined, at least, byDetermines probability of a blocking, mapping telemetry data of onerunning back blocking, running, and/or or more players during eachreceiving, or running for each receiving during respective play.respective formation, etc. a passing play Probability of Determined, atleast, by Determines preferred routes for a completion comparingtelemetry data of receivers, probability of a one or more players at acompletion per route, per start of a play. formation, per opposingplayer matchups, etc. Probability Determined, at least, by Determinesprobability of a of breaking mapping telemetry data over a pass and/orroute being an out- route period of time. breaking route (e.g., towardsa side line) or an in-breaking route (e.g, towards a middle of a field),etc. Probability Determined, at least, by Determines how often each ofopen comparing telemetry data for receiver is open for each routereceiver each receiving and each and/or against each defender defenderfor a respective (e.g, success rate in man formation. coverage against arespective defender), etc. Probability Determined, at least, byDetermines expected defense of blitz comparing telemetry data of blitztendency for each one or more players at a respective formation (e.g,start of a play. probability of blitz in a passing formation,probability of blitz in a rushing formation, etc.), etc. Third downsDetermined, at least, by Determines if a player is used played comparingtelemetry data of in third down situations (e.g, one or more playerswith a an RB is a “third down back”), game clock. etc. ProbabilityDetermined, at least, by Determines expected rushing of rushing mappingtelemetry data of routes (e.g, rushing away from direction one or moreplayers during offensive line, rushing through a period of time.offensive line, rushing towards sideline, etc.), etc. ProbabilityDetermined, at least, by Determines probability of an of a pull mappingtelemetry data of offensive lineman pulling to one or more players overa other side to of the offensive period of time. line (e.g, pulling),etc. Route Determined, at least, by Determines each expected combinationcomparing telemetry data receiver or rushing route expectations at astart of a player with combination for a respective historical formationdata. formation, etc. Rusher time Determined, at least, by Determinesprobability that a behind line mapping telemetry data of rusher isblocked at the line of of scrimmage one or more players over ascrimmage, average amount of period of time. time a defender is acrossthe line of scrimmage, etc. Sacks allowed Determined, at least, byDetermines probability of a in respective comparing telemetry data sackper formation (e.g., sacks situations for each play. in rushingformation, sacks in passing formation), etc. Tackles Determined, atleast, by Determines probability of location comparing telemetry data atan location of tacking a ball end of a play. carrier, etc. Targets byDetermined, at least, by Determines probability of a coverage typecomparing telemetry data from catch by each type of coverage, one ormore player with probability of a being targeted telemetry data from theball with a pass by each type of during a period of time. coverage, etc.Targets by Determined, at least, by Determines probability of a routecomparing telemetry data from catch by each route, probability one ormore player with of a being targeted with a pass telemetry data from theball by each type of route, etc. during a period of time. Tendency toDetermined, at least, by Determines expected type of chop block mappingtelemetry data from block. two or more opposing players during a periodof time. Tendency to Determined, at least, by Determines probability aplayer juke, spin, telemetry data from one or will juke, spin, stiffarm, etc. stiff arm, more players over a period of if facing a defender.etc. time. Tendency to Determined, at least, by Determines probabilityof a sweep mapping telemetry data from wide receiver going into motionone or more offensive players for each formation, etc. during a periodof time. Three down Determined, at least, by Determines probability of apatterns comparing telemetry data with pattern of passing plays and/orhistorical formation data. rushing plays (e.g, Pass-Pass- Pass,Pass-Pass-Run, Pass-Run- Pass, Pass-Run-Run, etc.) Time to breachDetermined, at least, by Determines probability of sack, line ofscrimmage comparing a period of time to amount of time to complete a fora sack and/or breach line of scrimmage with pass before a sack, etc.pass a period of time to pass the ball per formation. Yardage perDetermined, at least, by Determines how each route is route mappingtelemetry data from run (e.g., a shallow route, a one or more playersover a deep route, etc.), average period of time. yardage per completedroute, etc.

TABLE 5 Covariates derived from historical play- by-play data publishedby the NFL. Statistic (covariate) Description Games The number of gamesa player has played at a given position, the number of games a playerhas played in total, etc. Games Started The number of games in which aplayer has started in a game at that position. Passing Attempts Numberof times a player throws the ball forward, attempting to compete a pass.Passing Completions Number of times a player completes a pass to anotherplayer that is eligible to catch a pass. Passing Yards Total yardsgained passing the ball for each play, for each formation, for eachgame, etc. Passing Touchdowns Number of completed passes resulting in atouchdown. Interceptions If a player intercepts a pass from theoffensive player who threw it. Longest Pass Total yards of the longestpass play. Sacks Allowed Number of times the quarterback is tackledbehind the line of scrimmage. Sack Yardage Total number of yards lost ifthe quarterback was sacked by the defense. Fumbles Number of times theplayer drops the football before a play is completed. Fumbles LostNumber of times the player loses possession of the football afterfumbling the ball. Completion Percentage of completed passes. PercentageYards per Passing Average number of yards gained per Attempt passingattempt. Touchdown Percentage of pass attempts that result Percentage ina touchdown. Interception Percentage of pass attempts that resultPercentage in an interception. Rushing Yards Total rushing yards gainedby a player. Rushing Attempts Number of times the player attempted torush. Rushing Touchdowns Number of completed rushes resulting in atouchdown. Longest Rush Total yards of the longest rushing play. Yardsper Rushing Average yards a player gains across the Attempt rushes theplayer conducted. Receptions Number of times a player catches a forwardpass. Catches Number of times a player catches a forward or lateralpass. Receiving Yards Total yards gained in catching the ball. Yardsafter Catch The forward yardage gained from the spot of the receptionuntil the receiver is downed, runs out of bounds, scores, or loses theball. Yards per Reception Average yards per reception. Dropped PassesNumber of catchable balls a receiver drops. Tackles Solo Number of timesa player singlehandedly takes down the ball carrier. Tackles AssistNumber of times a player takes down a ball carrier with help fromanother player. Tackles for Loss Solo Number of solo tackles made by aplayer for a loss of yards. Tackles for Loss Number of assisted tacklesfor a loss of Assist yards. Does not include sacks. Tackles for LossTotal yards lost from tackles made by a Yards player. Sacks Solo Numberof times a single player tackles the quarterback behind the line ofscrimmage. Sacks Assist Number of times a player sacks the quarterbackwith help from another player. Sack Yards Total yards lost from sacks.Passes Defended Any pass that a defender, through contact with thefootball, causes to be incomplete. Forced Fumbles Number of times aplayer forces the player with the ball to lose it. Fumble RecoveriesNumber of times a player recovers a loose ball. Fumble Return Yards Theyards accumulated after a ball has been fumbled, then recovered. HurriesNumber of times a player forces the quarterback to throw the footballbefore the quarterback is ready. Safeties Number of times a playerscores two points by tackling an opponent in possession of the ball inhis own end zone. Blocks Number of times a player blocks a punt or kick.Interception Return Number of yards compiled by a defensive Yardageplayer returning one or more interceptions. Average Yards per Averagenumber of yards gained after Interception intercepting the football.Field Goals Made Number of good kicks between the goal posts. FieldGoals Attempted Number of attempts by kicker to kick the ball betweenthe goal posts. Field Goal Percentage Percentage of kicks made by aplayer that score points. Field Goal Long Longest kick made by a playeror team. Field Goals Blocked Number of kicks a player or team. KickoffsTotal number of times a kicker kicked off. Kickoff Yards Total yardsfrom kickoffs made by a kicker. Kickoffs Out of Number of kickoffs thatgo out of bounds. Bounds Kickoff Yards The average yards of kickoffsmade by a Average kicker. Kickoff Touchbacks The number of kicks thatland in the end zone or end up rolling into the end zone and are notreturned. Kickoff Touchback Percentage of kickoffs that result in aPercentage touchback. Onside Kicks Number of times a kicker attempted a10-yard kick in hopes of being recovered by the kicking team. OnsideKicks Number of 10-yard kickoff attempts Recovered recovered by thekicking team. Punts Number of punts made by a player. Punting YardsTotal punt yards made by a player. Yards per Punt Average yards per puntmade by a player. Longest Punt Longest punt made by a player. PuntTouchbacks Plays in which the ball is ruled dead on or behind a team'sown goal line after a kickoff, punt, interception, or fumble. PuntingTouchback Percentage of punts resulting in a touchback. PercentagePunting Inside 20 Punts downed inside the 20-yard line. Punting Inside20 Percentage of punts downed inside the Percentage 20-yard line.Blocked Punts Number of punts blocked by a player. Punts Returned Numberof punts returned. Punts Returned Total yardage returned. Yardage Yardsper Punt Return Average yards per return. Kick/Punt Return Yardsreturned. Yards Kick/Punt Return Number of kicks and/or punts returned.Attempts Kick/Punt Return Returns resulting in a touchdown. TouchdownsKick/Punt Return Fair Player returning a punt signals by waving Catchesis extended arm from side to side over his head, making it illegal forthe opposition to tackle him. Longest Kick/Punt Longest single returnyardage. Return Yards per Kick/Punt Average Return Yardage. ReturnPoints Scored Points for (scored). Points Allowed Points against(allowed). Total Yards Total yards (pass and rush combined). Time ofPossession Amount of time a team has possession of the football. TotalNumber of First Total number of first downs. Downs Third Down Number orpercentage of third down Conversions conversions made by a team. ThirdDown Attempts Number or percentage of third down attempts made by ateam. Fourth Down Number or percentage of fourth down conversionsConversions made by a team. Fourth Down Number or percentage of fourthdown attempts Attempts made by a team. Turnovers Number of turnoverscommitted and/or received by a team. Number of penalties Number ofpenalties committed by the team committed or player. Total yards fromTotal yards from penalties committed by penalties committed the team orplayer.

In some embodiments, external covariates are included in the modelsimilarly to internal covariates, while in other embodiments externalcovariates are omitted from the covariate analyses and model.

In various embodiments, the present competition is a football game andthe point is further determined by one or more internal covariatesselected from the group consisting of (i) a season-to-date summary ofthe passing yards of the first team and the second team, (ii) aseason-to-date summary of the rushing yards of the first team and thesecond team, (iii) a season-to-date fumble rate of the first team andthe second team, and (iv) a season-to-date interception rate of thefirst team and the second team. In some embodiments, the present game isa football game and the point is further determined by whether the firstteam or the second team is playing on their home field in the presentgame. In some embodiments, the one or more internal covariates includeone or more covariates listed in Table 5, which includes statisticalplayer data published by the NFL.

In various embodiments, the win/loss data for the plurality ofhistorical games includes the first covariate parameter for one or moreplayers from respective historical games in the plurality of historicalgames. The first covariate parameter in the win/loss data is derivedfrom time-stamped position information of the one or more playerscaptured by a telemetry tracking system during respective historicalgames in the plurality of historical games. The first covariateparameter in the win/loss data is used, in part, to determine thecalculation of the team strength of the first team and the team strengthof the second team.

In some embodiments, for a football game, the first covariate parameter(e.g., covariates listed in Table 4) is determined for a quarterback, arunning back, a wide receiver, a tight end, a center, an offensiveguard, an offensive tackle, a middle linebacker, an outside linebacker,a defensive end, a defensive tackle, a cornerback, a safety, a kicker, aholder, a long snapper, a punter or kick/punt returner of the first teamor the second team. In some embodiments, the first covariate parameteris determined for a quarterback of the first team or the second team andthe first covariate parameter is aggressiveness or a distance between aplayer and the quarterback per pass attempt. In some embodiments, thefirst covariate parameter is determined for a wide receiver of the firstteam or the second team and the first covariate parameter is a cushionbetween the wide receiver and defensive backs, a distance travelled,yardage per route, or a percentage outbreaking route. In someembodiments, the first covariate parameter is determined for a tight endof the first team or the second team and the first covariate parameteris a distance travelled, missed blocks, or a percentage outbreakingroute. The covariates listed in Table 4 provide a more detaileddescription of positions and/or movement of the players in the field,compared to the conventionally derived covariates listed in Table 5.

In some embodiments, the plurality of historical games spans a pluralityof seasons over a plurality of years. In some embodiments, the pluralityof historical games comprises fifty games. In some embodiments, theplurality of historical games comprises one thousand games.

In some embodiments, the plurality of teams consists of between five andfifty teams (e.g., 32 team for the NFL). In some embodiments, theplurality of teams consists of between five and thirty teams. The firstplurality of players comprises twenty players and only a subset of theplurality of players is playing in the present game at any given time.The first plurality of players comprises thirty players and wherein onlya subset of the plurality of players is playing in the present game atany given time. For example, in football, 11 players from each team areon the field at a given time.

In some embodiments, the calculating and predicting is repeated at anadditional time point. When the present game is football, the time pointis before a first play in the present game and the additional time pointis after the first play in the present game. In some embodiments, thepresent game is football and the time point is after a first play in thepresent game and the additional time point is after a second play in thepresent game. In some embodiments, the present game is football and thecalculating and predicting is repeated after each play in the presentgame. For example, the prediction of the outcome of the present game iscalculated after each play in the present game. Each prediction of theoutcome reflects the first covariate parameter for each of the player atrespective time points after each play. Quantified changes in the firstcovariate (e.g., h_(i)) shifts the center of the continuous distribution(see, Equation 10 above) accordingly. In some embodiments, the presentgame is football, and the calculating and predicting is performed at theend of the first quarter, at the end of the second quarter and at theend of the third quarter.

In some embodiments, the method 800 is performed by a device executingone or more programs (e.g., one or more programs stored in memory 502 ofstatistics system 500 in FIG. 5) comprising instructions to perform themethod 800. In some embodiments, the method 800 is performed by a systemcomprising at least one processor (e.g., CPU 574) and memory (e.g., oneor more programs stored in memory 502 of statistics system 500)comprising instructions to perform the method 800.

In some embodiments, the model described herein is applied to machinelearning techniques (e.g., the model is processed by a machine learningengine 210 described below with respect to FIG. 2A). A set of internalcovariates derived from the tracking system described with respect toFIGS. 1-7 is used as a training set (e.g., historical training data 214)to develop algorithms for producing covariant parameters that describeabilities of each player type. The algorithms are further extended toproduce a prediction for a team strength and/or outcome of a game.

The model described herein can be applied to a large amount of internalcovariates for all player positions of a team with the internalcovariates derived from the tracking system described with respect toFIGS. 1-7. The model applied on such variables provides a more detaileddescription of conventional parameters used to describe a player'sability. For example, conventionally rushing yards is a parameterrecorded from the position where the quarterback or direct snap playerhands off or carries the football immediately after receiving the snapfrom the center. Rushing yards are reduced to a single parameter, eithera positive or a negative number of yards, describing forward progressand reversed direction, respectively. However, with the methods andsystems of the present invention, this parameter could be broken intoseveral parameters that describe the play at more detailed level. Forexample, the rushed yards could be broken into two parameters-onedescribing how much the player was pushed back and another describinghow much the player made forward progress. Additionally, parametersdescribing leftward and rightward movement of the player, or speed ofthe player could be considered.

The model described herein can be easily applied to include internalcovariates related to all players of the team. For example, for rushingplays, the conventional statistical play-by-play data includes datarelated only to a rusher and tackler(s) and for passing plays theconventional statistical play-by-play data includes data related only toa passer, a targeted receiver, tackler(s), and interceptor. With themethods and systems described in the present disclosure, theplay-by-play data includes data related to all the players on the field.

Although some of the examples above use a single covariate parameters,the process may instead use several covariate parameters. In suchembodiments, additional covariates that do not require the time-stampposition information acquired through telemetry can be used in additionthe covariate parameters (first covariates that are based on suchtelemetry data). Thus, for instance, in some embodiments two covariatesor more, where one of the covariates is derived from the telemetry dataone of covariates is derived from sources other than the telemetry data(e.g., a player's draft pick number, a players height, weight, and age,or any of the covariates listed in Table 5 are all examples ofcovariates that do not require the telemetry data).

While the present disclosure describes various systems and methods inrelation to a gridiron football game, one skilled in the art willrecognize that the present disclosure is not limited thereto. Thetechniques disclosed herein find application in games with a discrete orfinite state where a player or team has possession of a ball (e.g.,holding the ball) as well as other types of events. For instance, insome embodiments the systems and methods of the present disclosure areapplied to events including a baseball game, a basketball game, acricket game, a football game, a handball game, a hockey game (e.g., icehockey, field hockey), a kickball game, a Lacrosse game, a rugby game, asoccer game, a softball game, or a volleyball game, auto racing, boxing,cycling, running, swimming, tennis etc., or any such event in which alocation of a subject is relevant to an outcome of the event. Forexample, for baseball the calculating and predicting is repeated aftereach inning, other than the final inning in the present game.

The present disclosure addresses the need in the art for improvedsystems and methods for evaluating team strengths and players'abilities. In particular, the present disclosure provides for predictingan outcome of a live sport event based on positional and kinetic datarecorded by a player tracking system during the live sport event. Thepresent disclosure facilitates increased spectators' engagement andinterest in the live sport event by providing updated predictions of theoutcome while the sport event is ongoing.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. A system to predict an outcome of a competitionbetween a first competitor that includes a first set of one or moreparticipants and a second competitor that includes a second set of oneor more participants, the system comprising: a communication interfaceconfigured to receive time-stamped position information of one or moreparticipants of one or both of the first set of participant(s) and thesecond set of participant(s) in the competition, the time-stampedposition information captured by a telemetry tracking system during thecompetition; and a processor coupled to the communication interface andconfigured to: calculate while the competition is ongoing a firstcovariate parameter for each of one or more participants in one or bothof the first set of participant(s) and the second set of participant(s)at a point in time, wherein each respective first covariate parameter isderived from the time-stamped position information of a correspondingparticipant of the first or second set of one or more participants inthe competition at the point in time; and predict the outcome of thecompetition, as of the point in time, based at least in part on (i) adifference between a calculated competitor strength of the firstcompetitor and a calculated competitor strength of the second competitorbased at least in part on historical data associated with the first andsecond competitors, respectively, and (ii) the calculated firstcovariate parameter(s).
 2. The system of claim 1, further comprising thetelemetry tracking system is configured to determine the time-stampedposition information for each participant based on signals received fromtracking devices, wherein a pair of tracking devices uniquely andindependently tracks a respective participant.
 3. The system of claim 1,wherein the time-stamped position information captured by a telemetrytracking system includes position data of a ball or other equipment. 4.The system of claim 1, wherein the time-stamped position includes atleast an x-coordinate and a y-coordinate.
 5. The system of claim 1,wherein the first covariate parameter is calculated at least in part byusing position information to calculate a derivative with respect totime including at least one of velocity, acceleration, and jerk.
 6. Thesystem of claim 1, wherein the competition is between a first teamcomprising a first subset of the first set of participants and a secondteam comprising a second subset of the second set of participants, andthe competition is a game.
 7. The system of claim 1, wherein theprocessor is further configured to predict the outcome including bycomparing a first value for the first covariate parameter calculated forthe first competitor with a second value for the first covariateparameter calculated for the second competitor.
 8. The system of claim7, wherein comparing the first value for the first covariate parameterwith the second value for the first covariate parameter includescalculating a difference between the first value and the second value.9. The system of claim 1, wherein the first covariate parametercorresponding to a first participant included in the first set ofparticipant(s) is determined based at least in part on positioninformation of the first participant relative to one or moreparticipants included in the second set of participant(s).
 10. Thesystem of claim 1, wherein the processor is further configured topredict the outcome including by evaluating a continuous distributioncentered at a point that is determined, at least in part, by (i) adifference between a calculated competitor strength of the firstcompetitor and a calculated competitor strength of the second competitorbased upon win-loss data included in the historical data and (ii) eachfirst covariate parameter.
 11. The system of claim 1, wherein the firstcovariate parameter is one of a plurality of first covariate parametersused to predict the outcome.
 12. The system of claim 1, wherein thefirst covariate parameter is based at least in part on telemetry of thecompetition.
 13. The system of claim 1, wherein the first covariateparameter is based at least in part on historical telemetry.
 14. Thesystem of claim 1, wherein the first covariate parameter is based atleast in part on the first competitor or the second competitor.
 15. Thesystem of claim 1, wherein the first covariate parameter is external tothe first competitor and external to the second competitor.
 16. Thesystem of claim 1, wherein the processor is further configured tocalculate the difference between the first competitor strength and thesecond competitor strength based at least in part on the historicaldata, wherein the historical data includes win/loss data among aplurality of competitors comprising a set of competitors.
 17. The systemof claim 1, wherein the outcome is a predicted final score difference atan end of the competition.
 18. The system of claim 1, wherein theprocessor is further configured to predict an outcome of a portion ofthe competition.
 19. The system of claim 1, wherein the first covariateparameter is calculated while the competition is ongoing.
 20. A methodto predict an outcome of a competition between a first competitor thatincludes a first set of one or more participants and a second competitorthat includes a second set of one or more participants, the methodcomprising: receiving time-stamped position information of one or moreparticipants of one or both of the first set of participant(s) and thesecond set of participant(s) in the competition, the time-stampedposition information captured by a telemetry tracking system during thecompetition; calculating while the competition is ongoing a firstcovariate parameter for each of one or more participants in one or bothof the first set of participant(s) and the second set of participant(s)at a point in time, wherein each respective first covariate parameter isderived from the time-stamped position information of a correspondingparticipant of the first or second set of one or more participants inthe competition at the point in time; and predicting the outcome of thecompetition, as of the point in time, based at least in part on (i) adifference between a calculated competitor strength of the firstcompetitor and a calculated competitor strength of the second competitorbased at least in part on historical data associated with the first andsecond competitors, respectively, and (ii) the calculated firstcovariate parameter(s).
 21. A computer program product embodied in anon-transitory computer readable storage medium and comprising computerinstructions for: receiving time-stamped position information of one ormore participants of one or both of the first set of participant(s) andthe second set of participant(s) in the competition, the time-stampedposition information captured by a telemetry tracking system during thecompetition; calculating while the competition is ongoing a firstcovariate parameter for each of one or more participants in one or bothof the first set of participant(s) and the second set of participant(s)at a point in time, wherein each respective first covariate parameter isderived from the time-stamped position information of a correspondingparticipant of the first or second set of one or more participants inthe competition at the point in time; and predicting the outcome of thecompetition, as of the point in time, based at least in part on (i) adifference between a calculated competitor strength of the firstcompetitor and a calculated competitor strength of the second competitorbased at least in part on historical data associated with the first andsecond competitors, respectively, and (ii) the calculated firstcovariate parameter(s).